{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>031</td>\n",
       "      <td>510300</td>\n",
       "      <td>17031510300</td>\n",
       "      <td>5103</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2958348</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-87.58745 41.72327, -87.58699 41.723...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>031</td>\n",
       "      <td>520100</td>\n",
       "      <td>17031520100</td>\n",
       "      <td>5201</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2581898</td>\n",
       "      <td>521681</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-87.54292 41.72391, -87.54276 41.724...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>097</td>\n",
       "      <td>863006</td>\n",
       "      <td>17097863006</td>\n",
       "      <td>8630.06</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>207264</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-87.85621 42.29921, -87.85604 42.299...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>097</td>\n",
       "      <td>863004</td>\n",
       "      <td>17097863004</td>\n",
       "      <td>8630.04</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>4440910</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>354</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>POLYGON ((-87.87635 42.30871, -87.87565 42.308...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>097</td>\n",
       "      <td>866200</td>\n",
       "      <td>17097866200</td>\n",
       "      <td>8662</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>32717452</td>\n",
       "      <td>584438</td>\n",
       "      <td>...</td>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>327</td>\n",
       "      <td>0</td>\n",
       "      <td>356</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>POLYGON ((-87.94467 42.24049, -87.94088 42.240...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        17        031    510300  17031510300     5103  Census Tract   G5020   \n",
       "1        17        031    520100  17031520100     5201  Census Tract   G5020   \n",
       "2        17        097    863006  17097863006  8630.06  Census Tract   G5020   \n",
       "3        17        097    863004  17097863004  8630.04  Census Tract   G5020   \n",
       "4        17        097    866200  17097866200     8662  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20   ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   2958348         0  ...        0        0        0        0   \n",
       "1          S   2581898    521681  ...        0        0        0        0   \n",
       "2          S    207264         0  ...        0        0        0        0   \n",
       "3          S   4440910         0  ...        0        0        0        0   \n",
       "4          S  32717452    584438  ...      355        0       28      327   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        1        0        0        1   \n",
       "1        0        1        0        0        1   \n",
       "2        0      883        0      883        0   \n",
       "3        0      354      291        0       63   \n",
       "4        0      356      123        0      233   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-87.58745 41.72327, -87.58699 41.723...  \n",
       "1  POLYGON ((-87.54292 41.72391, -87.54276 41.724...  \n",
       "2  POLYGON ((-87.85621 42.29921, -87.85604 42.299...  \n",
       "3  POLYGON ((-87.87635 42.30871, -87.87565 42.308...  \n",
       "4  POLYGON ((-87.94467 42.24049, -87.94088 42.240...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/il_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e4af1be-0f6a-4c46-9d23-da70d63c30e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 3265 popn tracts for IL\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"IL\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population IL voter-age population\n",
      "state pop= 12812508 VAP pct Hispanic is  0.16203980431360906\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP IL\n",
      "total state pop= 12812508 , VAP pct Black is  0.1374015960247489\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for IL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for IL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "605 5693 0.00870533701650013 nonPolygon tract no, pop, area\n",
      "1.213900000042879e-08\n",
      "0.00870532487750013\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb68f44b-eb19-4993-b856-cfa9c855b2e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = 0\n",
    "for geom in tractGeom[605].geoms:\n",
    "    if counter ==1 :\n",
    "        fixedGeom = geom\n",
    "    counter +=1\n",
    "tractGeom[605] = fixedGeom\n",
    "plotPoly(tractGeom[605])\n",
    "notPoly[606] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for GA)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9999]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "isSkippedTract = [0]*nTracts\n",
    "lakeTracts = [9999]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y+0.00,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 9999:  #for Tennessee, these ALL appear to be interior\n",
    "            if lakeTracts == [9999]:\n",
    "                lakeTracts = [m]\n",
    "                isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if y > 34:  #m==99940 or m==999909: \n",
    "                    thisTract = \"interior\"\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "greatLakeTracts = [3235,2385]\n",
    "for gLT in range(len(greatLakeTracts)):\n",
    "    t = greatLakeTracts[gLT]\n",
    "    isSkippedTract[t]=1\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3265 2\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>VTDST20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREACAR</th>\n",
       "      <th>G20PRESLAR</th>\n",
       "      <th>G20USSDDUR</th>\n",
       "      <th>G20USSRCUR</th>\n",
       "      <th>G20USSIWIL</th>\n",
       "      <th>G20USSLMAL</th>\n",
       "      <th>G20USSGBLA</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>019</td>\n",
       "      <td>CN0100</td>\n",
       "      <td>17019CN0100</td>\n",
       "      <td>Cunningham 1</td>\n",
       "      <td>753</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>684</td>\n",
       "      <td>51</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON ((-88.23247 40.13302, -88.23175 40.134...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>019</td>\n",
       "      <td>CC0600</td>\n",
       "      <td>17019CC0600</td>\n",
       "      <td>City of Champaign 06</td>\n",
       "      <td>1035</td>\n",
       "      <td>264</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>958</td>\n",
       "      <td>253</td>\n",
       "      <td>56</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>POLYGON ((-88.25798 40.13331, -88.25798 40.134...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>019</td>\n",
       "      <td>CC0100</td>\n",
       "      <td>17019CC0100</td>\n",
       "      <td>City of Champaign 01</td>\n",
       "      <td>590</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>532</td>\n",
       "      <td>28</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-88.24012 40.11728, -88.24012 40.117...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>019</td>\n",
       "      <td>CC0900</td>\n",
       "      <td>17019CC0900</td>\n",
       "      <td>City of Champaign 09</td>\n",
       "      <td>618</td>\n",
       "      <td>98</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>578</td>\n",
       "      <td>84</td>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>POLYGON ((-88.27716 40.13611, -88.27702 40.136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>019</td>\n",
       "      <td>CC0300</td>\n",
       "      <td>17019CC0300</td>\n",
       "      <td>City of Champaign 03</td>\n",
       "      <td>1073</td>\n",
       "      <td>209</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1007</td>\n",
       "      <td>232</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>POLYGON ((-88.23540 40.11265, -88.23359 40.112...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 VTDST20      GEOID20                NAME20  G20PREDBID  \\\n",
       "0        17        019  CN0100  17019CN0100          Cunningham 1         753   \n",
       "1        17        019  CC0600  17019CC0600  City of Champaign 06        1035   \n",
       "2        17        019  CC0100  17019CC0100  City of Champaign 01         590   \n",
       "3        17        019  CC0900  17019CC0900  City of Champaign 09         618   \n",
       "4        17        019  CC0300  17019CC0300  City of Champaign 03        1073   \n",
       "\n",
       "   G20PRERTRU  G20PRELJOR  G20PREGHAW  G20PREACAR  G20PRESLAR  G20USSDDUR  \\\n",
       "0          62           7           9           2           5         684   \n",
       "1         264          16          16           4           4         958   \n",
       "2          34           2           2           1           6         532   \n",
       "3          98           6           8           2           1         578   \n",
       "4         209          28           9           3           6        1007   \n",
       "\n",
       "   G20USSRCUR  G20USSIWIL  G20USSLMAL  G20USSGBLA  \\\n",
       "0          51          70          12          15   \n",
       "1         253          56          21          35   \n",
       "2          28          58           5           4   \n",
       "3          84          37          15          14   \n",
       "4         232          10          35          18   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-88.23247 40.13302, -88.23175 40.134...  \n",
       "1  POLYGON ((-88.25798 40.13331, -88.25798 40.134...  \n",
       "2  POLYGON ((-88.24012 40.11728, -88.24012 40.117...  \n",
       "3  POLYGON ((-88.27716 40.13611, -88.27702 40.136...  \n",
       "4  POLYGON ((-88.23540 40.11265, -88.23359 40.112...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/il_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10083 0.4134095626719308 5918806 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n",
      "working on tract 1800\n",
      "working on tract 2100\n",
      "working on tract 2400\n",
      "working on tract 2700\n",
      "working on tract 3000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic IL map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-89.7,36.8)\n",
    "pt2 = Point(-88,36.8)\n",
    "pt3 = Point(-88,37.8)\n",
    "pt4 = Point(-89.7,37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #in case we want to clip southern tip\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd63d574-1716-4885-b38b-1368b2fcc7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(tractGeom[3235])  #why were these two not dropped?\n",
    "plotPoly(tractMAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba2d07be-ba2f-4e67-bb42-9cf45fe86b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "greatLakeTracts = [3235,2385]\n",
    "for gLT in range(len(greatLakeTracts)):\n",
    "    t = greatLakeTracts[gLT]\n",
    "    tractMAP = tractMAP.difference(tractGeom[t])\n",
    "wholeMAP = tractMAP\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08d8ab03-d7dd-4a1c-b272-7a17795850c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(isSkippedTract))\n",
    "greatLakeTracts = [3235,2385]\n",
    "for gLT in range(len(greatLakeTracts)):\n",
    "    t = greatLakeTracts[gLT]\n",
    "    isSkippedTract[t] = 1\n",
    "print(np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  56 tracts w pop 169721.0  to reassign to tracts...\n",
      "[630, 631, 633, 1162, 1164, 1166, 1169, 1171, 1276]\n",
      "I have flagged  203 precincts to reassign to precincts...\n",
      "[67, 121, 287, 1499, 1500, 1544, 1545, 1565, 1572, 2050, 2055, 2070, 2080, 2425, 2426, 2428, 2429, 2443, 2446, 2447, 2449, 2452, 2453, 2482, 2486, 2488, 2632, 2633, 2634, 2635, 2636, 2637, 2641, 2642, 2643, 2644, 2648, 2649, 2650, 3212, 3214, 3215, 3216, 3218, 3230, 3233, 3235]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  169721.0 people (VAP of 135227.0 ) and  83466.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.07607361963190185 815\n",
      "100 0.6487603305785123 484\n",
      "200 0.8858560794044665 403\n",
      "300 0.684472049689441 805\n",
      "400 0.8452173913043478 575\n",
      "500 0.6063492063492063 630\n",
      "600 0.8746666666666667 375\n",
      "700 0.7910447761194029 67\n",
      "800 0.7107981220657277 1065\n",
      "900 0.6795918367346939 490\n",
      "1000 0.4889502762430939 362\n",
      "1100 0.5456595264937993 887\n",
      "1200 0.3640226628895184 706\n",
      "1300 0.6219512195121951 82\n",
      "1400 0.7402985074626866 335\n",
      "1500 0.6881572930955647 2187\n",
      "1600 0.6329113924050633 237\n",
      "1700 0.7248322147651006 149\n",
      "1800 0.8571428571428571 182\n",
      "1900 0.8842443729903537 311\n",
      "2000 0.5833333333333334 432\n",
      "2100 0.39819004524886875 221\n",
      "2200 0.8202676864244742 523\n",
      "2300 0.7875 480\n",
      "2400 0.6342412451361867 514\n",
      "2500 0.8 245\n",
      "2600 0.5934515688949522 733\n",
      "2700 0.0 0\n",
      "2800 0.5555555555555556 927\n",
      "2900 0.3970276008492569 471\n",
      "3000 0.4343220338983051 472\n",
      "3100 0.4446227929373997 623\n",
      "3200 0.23134328358208955 268\n",
      "3300 0.4892086330935252 1390\n",
      "3400 0.5883514313919053 1013\n",
      "3500 0.4646098003629764 1653\n",
      "3600 0.41491672700941346 1381\n",
      "3700 0.5350957155879672 1097\n",
      "3800 0.11347517730496454 564\n",
      "3900 0.25833333333333336 480\n",
      "4000 0.23593964334705075 729\n",
      "4100 0.05128205128205128 39\n",
      "4200 0.05357142857142857 224\n",
      "4300 0.2777142857142857 875\n",
      "4400 0.211864406779661 590\n",
      "4500 0.39092055485498106 793\n",
      "4600 0.40795287187039764 679\n",
      "4700 0.13197026022304834 538\n",
      "4800 0.4927536231884058 759\n",
      "4900 0.7381756756756757 592\n",
      "5000 0.22875816993464052 765\n",
      "5100 0.10973936899862825 729\n",
      "5200 0.4222560975609756 656\n",
      "5300 0.5064935064935064 308\n",
      "5400 0.1544943820224719 356\n",
      "5500 0.3382663847780127 946\n",
      "5600 0.4306569343065693 685\n",
      "5700 0.6943907156673114 517\n",
      "5800 0.032806804374240585 823\n",
      "5900 0.16156670746634028 817\n",
      "6000 0.5092592592592593 324\n",
      "6100 0.026402640264026403 303\n",
      "6200 0.0708502024291498 494\n",
      "6300 0.5423423423423424 555\n",
      "6400 0.3047945205479452 584\n",
      "6500 0.18825301204819278 664\n",
      "6600 0.05571847507331378 341\n",
      "6700 0.04585152838427948 458\n",
      "6800 0.050100200400801605 499\n",
      "6900 0.03734439834024896 482\n",
      "7000 0.03495145631067961 515\n",
      "7100 0.23047375160051217 781\n",
      "7200 0.34269662921348315 890\n",
      "7300 0.45394736842105265 608\n",
      "7400 0.4329896907216495 873\n",
      "7500 0.5764192139737991 687\n",
      "7600 0.3987341772151899 948\n",
      "7700 0.5 680\n",
      "7800 0.35027726432532347 1082\n",
      "7900 0.42771084337349397 664\n",
      "8000 0.32007952286282304 503\n",
      "8100 0.4859504132231405 605\n",
      "8200 0.3791748526522593 509\n",
      "8300 0.49 500\n",
      "8400 0.33653846153846156 624\n",
      "8500 0.3794749403341289 419\n",
      "8600 0.44680851063829785 376\n",
      "8700 0.5083798882681564 537\n",
      "8800 0.5521126760563381 355\n",
      "8900 0.5173267326732673 404\n",
      "9000 0.7135416666666666 192\n",
      "9100 0.4341279799247177 797\n",
      "9200 0.5508166969147006 1102\n",
      "9300 0.46051032806804376 823\n",
      "9400 0.6296296296296297 81\n",
      "9500 0.25951557093425603 289\n",
      "9600 0.605410447761194 1072\n",
      "9700 0.4897360703812317 341\n",
      "9800 0.6053639846743295 522\n",
      "9900 0.5089020771513353 674\n",
      "10000 0.32486631016042783 748\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 15.414988508913016 14.6107727678985\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA6KUlEQVR4nO3dd1hUV/rA8e+ZoYN0sCvYK/beiNEkGpOY3stmEzdlTW+aXk01ppuyycaUTWLKL5qmxgQbNrCiqKAiKiqoSC9Tzu+POyAqKCjDzMj7eZ555vb7zhXfOXPuuecorTVCCCE8j8nVAQghhDg9ksCFEMJDSQIXQggPJQlcCCE8lCRwIYTwUF4NebLIyEgdExPTkKcUQgiPl5ycfFBrHXX88gZN4DExMSQlJTXkKYUQwuMppXZVt1yqUIQQwkNJAhdCCA8lCVwIITyUJHAhhPBQksCFEMJDSQIXQggPJQlcCCE8VIO2Axdn5tUv1vDLgnxMJjCZQClXR2TEUPE6fr665abK5apyedX1AMpRrFAYn9OYVpXrqu4DFcc0ZkwmMJuV8W5SlfNmE5hMyjGtMJmN9eYqy6q+Hz2/MuI2qRPnFZiUwqRUZcwmVWX5Sbatur5iv+P3qZznuHnH+sr9HOuqnvf496rH5LjjHf9+vIp9jll23HYn26bqOUX9kgTuISb8O4Ff34t3dRhC1CO78aa08QJAH11W2/lTblvxdtx6pY/Zx+Rdxgsz9vPYzf3O4DM1LEngHsJmOzrtE72D6HYH6NqzjOEDAxnZrymDu7eothTkDHatMSmFXWvsdo3dMShIxXTV94rtqy6rGEPk+PUV01WPBRzdvobtqh7PrjU2u8Zqs2OzOd6rzNvs+pjlFcsslortjj2u3a7RVed1leWaymUA2g52NNrOMdtqu8bu2FbrKtP2o59LH7/ezjHnPX59xXXRldtq7I59Ks5fdR8jnuP2q+Z1vNosO9k2tXmvy7Y17csJ61T1+1Rsbj82Vq0Vm34byVvv7uGxm0/8PO5KEriH+PW9UXw6LoWv/+8gW1L82Le1NXtWtGLBx8Z63xZpfPBhOdef3wUfb7NrgxXCw2QdLKBl8xL8AqyuDqVOJIF7CJNJcdvFPbjt4qPLtu/N5edFGfz+dx5/fhLPrRfBrd7FBLXeTse4w1x7WROuGtuets1CXBe4EB4gNeMwWNtyzjn2U2/sRqQVigdr3zKMB67rw4KP4/ljxU7+9cJS+ly4GrO3jbW/DOKRm/oS0zyE8O5rePGzJPKLylwdshBux2qz88xbOwEIC/GsMq1qyEGN+/fvr6U3woaRnVvE+7M3s2h5EYtmd0cXRWEO28Okh3fx8j39CQ70dXWIQriFloNWkLVqMK2HJrL5z/4E+fu4OqQTKKWStdb9T1guCfzsdzCvmDe+2MiMaWGUZnUioM1minZ1c3VYQrgF78hMvAOKKczo7LZNHWtK4FKF0ghEhgQw7d+D+HzWsc2qhBCgbWaCIgrcNnmfjCTwRuSDL/cB0KFXjtSHC+HgF5ZL7u5osnOLXB1KnUkCb0RmzxhIePdkNswZRViLw9zw+GK2Zh5ydVhCuNRTz5ZjPdyKHqM3U1hS7upw6kQSeCMSGRJAzoa+TPtvMv4Rh/jqpZF0aRuGb7PtRPVaTbdxi7ntuaX8tCiN4lKLq8MVokE8cmNfrntsGTnrBhDdeQc/L053dUi1JjcxGym7XfP5b5v56v9y2LzBj7wDwZRkt0CXhhobmMsIbJ1O686HiIq206OrN5eMbsHYATEeWVcoxKk8/sFKpj3cDl3WhLH/WMH3bw5xm9Za0gpFnJLVZmfu0u0sSMwmaa2FtJQQ8jJbo4vCqfixpgIOcd6NG/ljZrxLYxXCGVJ25HDBdensXTkE76idPP3KIR7/xwl5s8FJAhenrbTcyq+JO5j71wHm/BBI7ua+mIKy8QoopPPA3az6YRh+Pp71AIQQJ/PiZ0k8+2gElpxYonuvYuqjPky+qpfLfn1KAhf1orCknGsfSSR9m4mdG5tTtq8jr85aw8M39nV1aELUq/yiMq56cDnzv+yBLorEv3UqI8Yd4L5/tGHc4HYNGssZtwNXSpmVUmuVUr845l9TSm1RSm1QSv2klAqtx3iFmwry92HuO/GkzhtJbM994F3M+OGtXR2WEPUuONCXP2bGczArkBsfX4zWJuZ/FM/4+AiWp+x1dXhA3Vqh3AukVplfAPTQWscB24Ap9RmYcH+R0Xaw+vLk25sru3kV4mwTHuzPrBdGUrK7M5/MSQFt4vJbd7s6LKCWCVwp1Qq4EPikYpnWer7WuqLvxRVAq/oPT7izT1+KI6rXGn6aMYoh1y5ydThCON0/L+pB9zFr2be+B6kZB10dTq1L4DOAR6gcQuMEtwK/V7dCKTVJKZWklErKycmpe4TCbXVsHc5/348AIGVFtIujEaJhvDSlBVj8GTp+NwuTdrk0llMmcKXUBCBba51cw/rHASvwVXXrtdYfaa37a637R0VFnVGwwr18++dWLhzaDszlvDbdszrCF+J0XTy8A+fcsoQjqX0YM6AtfyW7LonXpgQ+DLhYKZUBfAOMVkp9CaCUuhmYAFyvG7I5i3ALWzPyAVC+Bdx1eZyLoxGi4fz1aTx9JhrVhnc/vgurzTUDQdSpGaFSKh54SGs9QSl1ATAdGKW1rlXdiDQjPLscKSwlsvUh7FYf7AXy60o0LlabHW+vo2XgHxPSuHRUR6ecyxndyb4LNAEWKKXWKaVmnsGxhAdqP3gTtiMtueKOza4ORYgG52U28dCbyyvnX3yn4ZsW1unxOa11ApDgmO7ghHiEh7hocgKHN8Uz9vYEvnst3tXhCOESr903hOVJCSz7Kp6Lzg9s8PPLk5iiTopLLfyybAfXTgxDeVkpPBAtj9GLRi1lRw79hx+hbF9HmnTYQMKv0fTt1Kxez1FTFYr8zxM1Opxfws+Ld/D7ooOsX2di97ZISva2A1tnANqNWoqfTwsXRymEa/VoF8We1EDa9tpMQXocAwdlsXHtQbrGRDr93JLAxTFKy608+f5qvvjCxIH1fcDWHTB6IQyLzaDHZcsZ1M+H8aOaMXbAUBdHK4R7iAwJoCijG5NfSeTdx4bSrUMZvtFphDTNJW1pL6d1SysJXByj3fAk9q0egil4H/0uSeScEb5MHN2aIT1aYjJFuDo8IdzaO48OZdTAbbz8bhapSdFkrxtIakYWg7o755eqJHBRaf/hQvatHoxX5C6Kslrh493c1SEJ4XGuOKcTV5zTieunLubraeDrY3bauWRINQHA7yt20KKl8TRlbK9MfLyd90cnRGNgsRgNRHyd+H9JErgA4Lp/5KG14pmPVrN53jBXhyOEx7M6epdwZistqUIRABTmRNC63yaevl1uTApRHyxWRwnciVUoksAbqfyiMhYmZbJ8/SE2byvFemQoeQdzXR2WEGcNi8V4lxK4qBfLU/by4sw0FvzQmvLstmDvCBh9N5hC9nHrpCLXBijEWUSqUMQpHcwrJnnLAVLSj7B1ZxEFBXbCQs2EhXoRFeaLv6+ZTekFfPu1F9nr+gMtCe64nkFjl9KzmzdDeoczLK45sS2aA9LqRIj6IglcVKvcYuPx91bx9ivRlO9vD8Sech8VlMOIGxcz5a42jBvcy/lBCtHIWa2AsmEySR14o5VfVMa23YfZviefNam5zP7BSsaq7uiSIXhFZDL61gQ6tvOiU2wgPTuEERHqx4HDxeTklpJzuIyiEiudY4K5cFgsQf7xrv44QjQaNhtgsgKSwN1acamFzRkHCfTzpnObCEwmVblu084c5i7O5K9lBWxc60/O9pZoqzfKbEN5WTB7l2P2seDlY8HsbcNmNWOzmCnLb4K1MBTKQjCqNozqDeWfS/shKYwfZ+bxf/YiOqyNSz6zEOLk7HZAObezQEngdVRcauHaR5axKtGf/INNKD0cgb2gKZX1x355+Edn4eVjoXB/c3RhFGAMduAdvZNW3XfhH2jDZlVYLCbKS81Yys1Yyrywlnth9rLhHWQhslUeYeEZREZpoqNMtGzmQ2zLAG4c34Ug/xEu+/xCiNoxOnqVBO5WJt6zjAUfx+MTvYMmTQ/TquMhmrdMpUVzE6WlmvR02JcZgNXiRYdBW+jWfRND+wdz9dj2tG0WS23qq4UQnk9rQDl3qDVJ4HVw1cOLWPBxPE06bCA/LQ5o5+qQhBBuqiGGWpBH6Wspv6iM2W/3IzJuNRnJzhn3TghxFtE4vQ5cEngt2O2ah95cDeVB3HSLlfBgf1eHJIRwc1IH7gZKy630vzSRTb+NxBy6l7uu6uLqkIQQHkCDlMBdadf+PEJjd7Lpt5H0uzyB/RlhtG8Z5uqwhBAeQOrAXezrP9IpyzLqu/39YfueI64NSAjhORqgCkUS+Ek8elNfXvg0iajeq1n65UgGx0XTacwStu+VXvuEECdnVKE49xxSB34SJpPi8X/05/F/wNINe7jn6e2snTOUjl2O0H3UIpQC/wBN317evPnQIKd2WiOE8DDa6flbSuC1NTyuFWt+GsW383cQ2mY3KQt7k7IwjlU/DGHm48MIjdnJq1+scXWYQgg30RCtUCSB19FV53bm8Ka+6JIQ7MVh2Mp9eOTtFdgsPjx6U2/iLlokVSxCCGmF4glMJsUrkwezLz2KnhOWsPGXUQy8YIerwxJCuJo7tUJRSpmVUmuVUr845q9USm1SStmVUv2dF6JniAwJYMPcUXhF7qIwJ9TV4QghXGxzZg66xLnNjutSAr8XSK0ynwJcBiyu14g8XI9hGZQfiCVt92FXhyKEcCHbhiudfo5aJXClVCvgQuCTimVa61St9VZnBeaJ7HZNzgEf8CmmddNgV4cjhHChpp13Ov0ctS2BzwAeAercN6JSapJSKkkplZSTk1PX3T3KpBeWsXfFEHqelyxNCoVo5Jp3zQDffKee45QJXCk1AcjWWiefzgm01h9prftrrftHRUWdziE8xo4MYxTTEcMkeQshnN0KvHYP8gwDLlZKjQf8gGCl1Jda6xucG5pn+GXZdt79YjebN/iwe01fvKN3MuWfPV0dlhDC1bTC5b0Raq2nAFMAlFLxwEONPXnnF5Xx4qdr+fJLyFo1EGiPV0QmLXtv4q2XI2gVJfXfQjR2BTlhoJ03oDGcwaP0SqlLgXcwBnz8VSm1Tmt9fr1F5obKLTZe+mwNLz0ZiSV7MMo/l+E3LGbmc93pHtsGkAGGhRCGJpF5UB7k1HPUKYFrrROABMf0T8BP9R+S+1mespfJz6SxdkEX7PkD8IrIZOr7K3n8H30J8It3dXhCCDdk9raCdzEQ4LRzyN22UygsKWf4cBP2vHia9VvJddfv5OlJfQkOlNK2EKJmyqRBO/dhd0ngp+Dn44W9KBTfFmnsSxrk6nCEEB7D+TcxpS+Uk7DbNV3PWwZWf7oOyHJ1OEIID2K3KjBZnXoOSeAn8di7K0n/awQ9Jyxi2TdDXR2OEMKD2G0mMNmceg5J4CdxIMcCwNjRPgT4ebs4GiGEJ7HZTCizxannkDrwKux2zYLVGcz6eTdLErzYs64HpiYHuOOKzq4OTQjhYWwNUIVy1ifwnVlH+H15JivX5bFrt439WSYOZ/tTVuIDgFIapYyxj/L3NsOeHwvEYgrZR8zATTw/JYKOrbu69kMIITyO3W5CObkK5axL4FabnTtfTOTnH305tLM19vxmQKhjrR1T0EF8ww/hG1AKgNbKeNkULXvsZMSoNG66pDVjB8RgMjV31ccQQng4u9UEZimB10pxqYW3v93ASy94U5A+HO+oncT2S6dz1y0M7B3IuYOa07dTUwL8ooFoV4crhDjL2W1SAq+Vz3/dzC0TY8HaDxVwmNufX8rMqcMwmWJdHZoQopGy25S0QqmNhFWHwOoPQNNuaVx5XitMJud35SiEEDUxWqFIFcopffzUMNq2XM0v84tInjOA8+PLSd1yiM5tIlwdmhCikdINUIVyVpTAvcwmnpk0gMT/jaB5743okjAOHC52dVhCiEbMJg/y1M2l9y1h3+rBjLwpgZG9W7s6HCFEI2a3mVBmSeC1FhludJ7eN87HxZEIIRo7m01hMtd5GOE68eg68OJSCx/8kMLXP+RxIMuXwiNNAFi/qdzFkQkhGjubFcxmFw+p5o6OFJZy4b9WsHxOd3RhH/AqwTdyLz5BxcRdvIj/Tuvj6hCFEI2czaow+0gCP0ZqxkEGjc2iID2eZv1XctNN27n/+h40C+/g6tCEEKKSzabwkhL4UQuTdnHBBQprXkcemL6cN+4f4uqQhBCiWjabCT8nZ1iPSeBWm53zzvXBXhzGJz+m88+LJHkLIdyX3WrCy0tG5AFgzD8XY89vTrvhSfzzoh6uDkcIIU7KZvHGy9u5rVA8IoEfKSxl0efxmIKyWfRdnKvDEUKIU9JWb7x8ZEg1PvppMwDDL9tMq6hgF0cjhBCnZrf4SgIHmD0nH4Cbroh0cSRCCFE7RglcqlBo2tR4t9mce0NACCHqjc0HLy95lJ7rL24GwE/zDrk4EiGEqB2tFSazc8/hEQn82vO6EN4jmflfdmNn1hFXhyOEEKdmN2N28rgEtU7gSimzUmqtUuoXx3y4UmqBUirN8R7mvDDh1Zd8sRdG84+p65x5GiGEqB/a7PS+UOpSAr8XSK0y/xiwUGvdEVjomHeaPp2NG5hL53QgZUeOM08lhBBnRGtAm9yjCkUp1Qq4EPikyuJLgM8d058DE+s1suP07dSMW55agq0ggl59bLzz3Xpnnk4IIU6b3dH4xF2qUGYAjwBV28Q01VrvA3C8VzvUu1JqklIqSSmVlJNzZiXnz54dwez5uzH7lnDPtd05/18JFJZI17FCCPdiczQ+Mbu6BK6UmgBka62TT+cEWuuPtNb9tdb9o6KiTucQx7jinE7s2BRB60GrmP9RPCFNc7n5ySVnfFwhhKgvlSVwVydwYBhwsVIqA/gGGK2U+hI4oJRqDuB4z3ZalMdpFRVMxtIhdDl/MfaCpsyaNgirzbkN5oUQorasVuPmpdns4ioUrfUUrXUrrXUMcA3wl9b6BmAOcLNjs5uBn50WZTVue24pW+aNxKfpdp6duR4vs0e0iBRCNAIWq1GgdIcSeE1eBsYqpdKAsY75BpO136hkGnz+bs4d2LwhTy2EECdVbjXyk5c7JXCtdYLWeoJj+pDW+lytdUfH+2HnhFi9H2cMIzJuNYtnxTO8Vyv8W25j6HUJfDInBbtdHrkXQrhOudXoxMrs5JoBj613CPDzJmf9AP5YsZMJ/07AL7iI5d8O5/ZLehDZcy17cvJdHaIQopGyWNy/CsUtnD8olrnvxJOb2ocduwuJ6JlE7ua+/HfuNleHJoRopCy2iioUKYHXWmyLUKKaFwNwx+XdXByNEKKxsmmjCkXhHg/yeIzycuMjNW2TR99LF/GfuVInLoRoWCZHZtV2KYHXSfLc/kx+NZGmnXexdu5gbru4B75Rexh8TQIzvllHucW5/fMKIYS3l5Fa7U5+POWsS+ChQX68/fBQslYNZtfeUm57dikhLQ+wcvYw7r+2N/6ROYy4MYE12/a7OlQhxFmqou7b5uTy4lmXwKtq0zSEj58azsEN/dm9v4T731hOZLtMln45kn49guk5YRH3v5HIb8t3SMlcCFFvTI5OrLR2n+5kPVqrqGCmPzCEA2sHsjBpN+2GrSHljyHMeGgoFw5th29AOU37rmLxut2uDlUI4eEqSuDOvv3m5dzDu6fR/dqy/e+25BeV8fvyrfyZmMPqZBvr/+jLxddsZ8/apgT5+7g6TCGEh6roRtbZdeCNMoFXCA705eoxnbl6TGcAJt6bwM9vxxPS9ABBzfYTGFpCSHg5MbF2Zj7dh7bNQlwcsRDCE1SWwOUmZsP58c1RvPBpEq3itmMy2zm8J4Kty7ryx8x4OvTI4+XPk6VJohDilCSBu4DJpHj8H/3ZtXQoual9KM3qiL0gig9/2ghoptzSj5YDV7k6TCGEmzObJIG7jUkTe3JgRzRthiWyP3kQbYYl8s5366U0LoSoVkU/4M7OEZLAayk82J+1v/Vh0NUJ7F7TnXuu7oVXyEFiRi7jigcX8f4PGzhSWOrqMIUQbqCiCkVreZTebYQH+7Pim3hy9ntzx4vLaNNnG5lrO/HD9FHcfUUcYcHeeEfspnn/lXzwwwYZr1OIRsqkpBWK24oMCeCDqcNgqvETaV36fr5fsIvE1SVk7PAmM7kLd10RwV3mMoLbrWfgqFxuvCya687rIiMHCdEIGA/y2CWBuzuTSdG3UzP6dmpWuSxt92E+mL2cpSvKSFnRlD8/iefPT+CWwIPE9N/CP2/24+Eb+uDj7eTOgoUQDcJu13z400YWrcrFaoX5c8KAOKcncOXsRz2r6t+/v05KSmqw87mLTTtzeO+bbcybp9m5qju6JAxT8H46DExj6FCYdm8fmoUHuTpMIUQdHc4v4dmP1vLh9CjK9nWsXG4O20NQ9EGmPm7nkRv7nvF5lFLJWuv+JyyXBN6w8ovKeP7jNXz1tWJ/Smd0SRj45dHngnV89WZ3usZEujpEIUQtdTx3Cel/jQAgus8qfv26NVpDz/ZR+PnUXwVHTQlcKmQbWHCgL6/dN4SsVYOxFoby6qw1tO2fwto5w+jWxYdm/VZy5cOLKC61uDpUIcQpvPrU0arTFm1K+Pnv3cS2CKnX5H0yUgJ3E78t38H9T+0lY0NLyrPb0f+KBFbPjnd1WEKIUxh1cwKLZ8VXzl/x0CJmvzaqXs8hJXA3N35IO7YuGEHZgXa0HLycpB+Hs2F7tqvDEkKcwqLP43nnu/WV8+cMDmuwc0sCd0P3/9sP7F48PzPV1aEIIWowZ2k64d3XYGqSw+SregHQpMMGbrigc4PFIAncDd17TS+8o3fy67fRMtCEEG7mYF4xwR03cMmIDuRu7osujOK8SQn8tnwH+WlxBAf6NlgsksDdkJfZxOW37KZkd1fCOm5l3sqdrg5JCOFww6OrKEiPq5xP2ZHDvA/jGTe4XYPHIgncTX01bQR3vLiM4l3duPKGAleHI4Rw6NvT/5j5Hl38+GROikt+LZ8ygSul/JRSq5RS65VSm5RSzzqW91JKLVdKbVRKzVVKBTs/3Mbj6/lbmPWhcTPE21eaFArhLp67YwDXPraYbuMWGwvKm3D7JT3wj8zhoskJDdoH0imbESqlFBCotS5USnkDS4F7gXeAh7TWi5RStwKxWusnT3YsaUZYO3a7xjt8P/a85oy/K4FvXx8qQ7wJ4YaOFJbywfebSNlWxNfTRgJw9SOL+OYVN2lGqA2Fjllvx0sDnQHHVxALgMvrKdZGz2RSPPjcLgAWzI7ht0SpAxfCHYUG+THlln589dJILp6cAEByUsPVTNfqTEops1JqHZANLNBarwRSgIsdm1wJtK5h30lKqSSlVFJOTk49hNw4vHrPYN77fgO2Mj+uHt+CrZmHTvtYVpudwpJyGYBCCCf6+/dwANL/GkHX8xfz4U8bOZhX7NRz1iqBa61tWuveQCtgoFKqB3ArcLdSKhloAlRb8aO1/khr3V9r3T8qKqqewm4c7ro8jnc/zYbyJjzwysY6738wr5ju4xfjHVhAkwAfzGaFMltQPsV4R2YS3Wc1/S9P4LbnlvL939ukyaIQZ+Ddt48+Pr9l/kjuuKwnUaEBTLw3wWnnrPOj9Eqpp4EirfXrVZZ1Ar7UWg882b5SB1535RYbUT1SyN/Wk0vvW8Ksl4bUqj48bfdhhl+STvba/rQ/J5HY9lYsFrBYoKwMsvf5kLMrgtIDbcHqZ+zkU0Bo+3SatirA20fj66dp01rRIdaXHh2DGRbXnPYtG+4pMyE8kd2uWZ26j3uf38bKb+MBONMeS2qqAz9ljytKqSjAorU+opTyB8YAryilorXW2UopE/AEMPPMQhTV8fE2s2FRDF0HbeWnGaMI/XwP19y5g1sva0t8nzaOjuMNRwpLefXz9Xz3g4Xty3pD+UAuuCOB3z+Ir/H45RYbfybt4JeEfSxZZmXn5gjSlndBaxO6LJBka9UmU3aadNjAwFGHmTAmlBvGdSIyJMBpn10IT2QyKV75JK0yeYd1WwOceZey1alNK5Q44HPAjFHl8p3W+jml1L3A3Y7NfgSm6FMcTErgp6/cYuOFT5N57YUQSvcYj+qamhwgunMGTULKKcjzYf/GLlAWgvLPpd3gTbzxTDMuGdnhtM9pt2u2780lKTWbjWl5LE8qYeXCppTs7mpsYLIQ2HYrXfoc5Nx4P26a0J7usVJNJhq3OUvTuWTE0f93r8xac8Z9gkt/4GcJq83O3KXbmf3HPlYsN7E3tQXWUn+8Aopp2z2Lm6/35/5rexHg5+20GLbvzeWLX9OZn1DEpuQw8nd0rqyGCem8jpQl7WgVJY8FiMap32UJrPkpHoCgdhsp2N7zjI8pCVw4TX5RGV/9sZW7b2mOLozi18QdjB/S8I8VC+EOikstTHl3NW8/PBSAx95ZybR/DzqjY0p3sqLeFZaU89SHqxh46Ur+fUszdGEULQaukOQtGrUAP2/eemgoD0xfDsDLkwfRtM8qpzQplAQu6sxu17w7ez0R7Xbz/B0D2bq4Fy3j0rnzpWVsWtjb1eEJ4RbeuH8IbYYlApC9biB/Je2u93NIAhd1MvmVRHyjM5l8VS/KcyO5/43lFOT6k7lsKO9PGUZokJ+rQxTCbTx8b2DldNtmTer9+JLARZ28+1Qc1kNtuW7KYvbuMTH9gdq1SxeisdmTk899k4xBymNHLaVPp6b1fg5J4KJOuo9ZA8C3M9uxctN+F0cjhHv44IcNXHh3Av0uS6DNsERCOq2ndSsTtiMtuX7qYnYkDMfH21zv522YoZPFWWPD3BHM+GYtD9/RguuuL6IoUx/zMJEQjclvy3fw4LN72DLP6IkQn0K8Q3IICM+j6+g1TL4tlDsvH+m080szQnFaYkYuY9eyQRQU2qUKRTRaylF2Ce+RzIrfYunYOtxJ55FmhOIM7cnJ586XlhHRI5ldS4YRN2GZJG/RKJWWWwnrtrZy/pUXfZ2WvE9GErg4pfyiMi68O4E2bW3MfHwYeXubMe7OBFbOHubq0IRocD8tSsM/uIgjqX0ql+0/WOqSWKQOXJzUdwu3ctONJsr2xRMZt5pnn/Jl0sQeeJlbujo0IRrc1PeOPlXp33oLGetbEx0WCJxQu9EgJIGLGi1P2cs1E5qifEp5+sPVPDNpgKtDEsJl9uTks3BxSeV8ZKvDRId1cWFEksBFDex2zUXX7UPbO/PnX2WM7ifJWzQeSVv2cftj2ziU7U1ZiRdHsiIpz2kLOh6wAyZatGm4wYtrIglcVOuhGSs4tHEIVz60iNH96neAViHcmd2uGTTQhL3A8Xfvm0/zuM10vCCT6yZG8K9LK3oXjHdViJUkgYtj/JW8iymv7mTVz33wbZHGly8Nd3VIQjQok0kx8vJUEv5rPDl5x1Mb+WCqe96wl1YoAoBXv1hDePc1nNu/Nau+H07zuFR+/tHLKU+PCeHOvlu4lZQ1Rh8mQ65N4L3Hhro4oppJCVww8qYElnwRjzl0L/G3LOa1RzvTv8tgV4clRIMot9h4+K2VLFtRTtrGMPK39UT55TPuzgR+nDHcrZ80lgTeyA24MoGk7+PpeO4SVv3fAEKDpHmgaDwWr9vNuEsKKM4cCl4lBLTYyehbF/PJ872JbRHv6vBOSRJ4I7Y6dR9J38fTdvgyUn4fKtUl4qxjt2vmLt1ORlYhE+NjsdrsXHrHBg7n+DBjWgS331VE8YH2TH41kdfvG4SPdzegm6vDrjVJ4I1YVk4RAOPHI8lbnHVWbsriouv3krPeaAJ7H+DXciule43WJVeOsYDdTETPNZXDn3kauYnZiPXpHAVepSSusLo6FCHqxfSv1+LXIo0L7khgcI8Wlcm7gqU4kH+9sBTv6J1g98YrfC8vPuvromjPnJTAG7E2TUNo0XcFGxbE8eUfqdxwQVdXhyTEGVmzqYCyfX2Y92HHymULk3aRlpmPUnDrRd3xMrfi3cfs7M3Jo3V0K0ym1i6M+MxICbyR+3B6FCbfEm6c0J5z/5nA0g17sNsbrothIepTh7b+ldPdxy+mrNzG6H5t+delPZk0sSdeZiPleZlNtG0W4tYtTGpDEngjN2FYe1I3+NFyQDJ/fRrPiF6tMAcUEN59DVc8uIjD+SWnPogQbuKRm3rTc8IiMFnZ9NtI/MJyMQXl4BWWxT2vJbo6vHonCVzQsXU4e5YP4fu/t3H91MX0GLOW4twm/DB9FNFtDzP5lUQplQuPEODnzYa5o3jvu820G7UUXRSJLorCdqQF388++9KdjMgjavTWN+uY8ogvJbu70qTDBi6/Lo/n/92LVlHBrg5NiGMcKSzl1c/Xk5ZRSuJiX7JWHfsgWnCn9Wxf1ZHIkAAXRXhmahqRRxK4OKlyi40bpi7l/z5vgyUnFryLaT1gHWPG2rjonGYczi8jrkME/To38/j6ROG5Oo9dwrY/R5yw/K5py3j2zj4em7grnHYCV0r5AYsBX4xWK99rrZ9WSvUGZgJ+gBW4S2u96mTHkgTuuex2zWe/bmLGh4fYtKgbujDqmPUqMIdLJ23mh+nSc6FoOLv25/HCRxuZ85MP2esGnrB+deo++ndp7oLI6teZJHAFBGqtC5VS3sBS4F7gOeBNrfXvSqnxwCNa6/iTHUsS+NnBbtf8vCSdBYnZRIR6s3VHCT99FoP1UFvaDEtkwgTNOYOiMJsUXdqG0TUm0tUhi7NUt3GLSf3j2FHfvaMyiInbzbQnmnJ5fCcXRVa/akrgp2wHro0MX+iY9Xa8tONVURkaAmTVT6jC3ZlMiktHdeTSUUfb2q65fT/X3r2EtOVdeH9ZFO9XrFA2wrslc9FlxUx/qD/hwf7VHlOI0/HgXeHc9sfR+Zk/bnT01x3jqpAaVK3qwJVSZiAZ6AC8p7V+VCnVFZgHKIzWLEO11ruq2XcSMAmgTZs2/XbtOmETcRYpt9iY9XsqW3cUYLPDyuQyVv/ZFkt2LCrwIKOuTGH86GDKLHYy9pSSuceK3Q5P/bs9I3t77gMVwnVm/b6Zm8d3wxy2h9KcFpVtvc8m9XITUykVCvwETMZIyou01j8opa4CJmmtx5xsf6lCaZzsds3b361n2ivl1dZTVpi/MoOxA2MaLjDhsaw2O78u20FGViH33dgFrH4ovyMcPOB7Vv7KqymB1+mrSmt9BEgALgBuBn50rJoN1Pw/UzRqJpPivmt6c2DtQFZt3sfrX649YZs+ExcxpGeLY5Ydzi9hztJ0CktOf+zB3JJc1u9ff9r7C/eyM+sI3y3cSt+JS5g4qgP3XdsbrH4AvPlZxlmZvE+mNjcxowCL1vqIUsofmA+8ArwG3Km1TlBKnQu8qrXud7JjSQlcVOh6wWK2zBtJi4Er+OCNSEb3a0OQvw9ZBwv4/JdtvDeznL1J/cDmA95FhHfeQq8BBTx3XweGx7Wq1TmSspIY8LHRmZG3yRuL3QLAmHZjeHrU08SGxtIyWPo/9yRNOmykcHvPatf5t0mlcGeXs7I565m0QokDPgfMGCX277TWzymlhgNvYdwILcVoRph8smNJAhcV7HbNkGsXseq7+KMLffOhPBC0GeWfS+/zN9C3j5nVyTa2JTWnNMtoUXDLE8vp3iGYnh1D6dzej5imEScc/60Vb/HA/AcwKzPxMfHENY1j8a7FrM5afcx27cLaMWX4FG7re5szP66oJw/PWM7r9w+pnPcK341PUAHFWbG0G5ZM2l/DJIE7iyRwcbyEtZl881smaTus5B5WBIdoxo4K4vaJXYkOCzxm24n3LObnd0aecIyIbhu48EI7d10XS9v2pbyw5HneW/0eADkP5xAZcLQZY4mlhE05m9h2aBt78/fy3OLnKCwvpG1IW8Z1GMfr571OoE/gCec4XQeLD7Izdyc+Zh/ah7cnyCeozsew2W2UWEvwNnnj6+W5XZ/Wh+JSC7c8mcjs14993qDtiGXsSBh6ViZvkAQuzhIH8g+xIT2HjelH2JJeQspGWP13c6wHuhgbhO6Ajr/BoLdJf/Z32oe3P+nxSiwlfLLmE+75457KZdf2uBar3cq5sefyr/7/qnVsq/auIsgniJTsFD5I+oB+zfvxxvI3jtnmnJhzGNl2JM/EP1O5LKsgi/TD6ezJ34PNbqOgvIAH5z9Iq+BWHCg8QEF5wTHHGNBiAOe1P4/cktzKaqG7B9xNr2a9qo3rYPFBMvMy2X54O8WWYg4UHWBn7k4eHPogHcI71PrzHa+grIAmvk1Oe//TMXrK2/z98j3Vrisps+Lnc3b2kC0JXJy1rHYrPy5PZtYPOcz7w4Q1dTx+fprUVEVMTO2OobXm1WWvMn/HfP7a+Vfl8oU3LSQ2NJYDRQdIyU6hd7Pe/Hfdf5m1fhYXdrqQRRmL6Nm0J/sK9rExe2O1x3502KO8v/r9YxLxlOFTAPhfyv/IOJJRY1yTB04mMiASfy9/UnJSmLV+VrXbPTjkQV4/73X25O/ht7TfeOrvp7DarRRbiimxVt+j5HPxz/HkqCdPdWkoLC8kMy+TzLxMftj8A7M2zKLcZtxYTrw1kSGtj1ZplFnLeDrhafLL8hnbbiwTOk3A2+x9ynMUW4rZnLOZ6cuns+3QNvy8/OgU0YnP1n0GgFmZUUphLTNDwrOQPAlKwyr379bNzqZNZ1/zwQqSwEWjcfHFMHcuXHcdREVBbi7ExxvzvrWogRj0ySBW7T1prxCVvExe9Ijuwbr96yqXxYbGMnngZBbsWMBTo55icKujHStNXTiVaUun4W0yklpFCfq3634jJjQGH7MP+wv3s27/Om7pfcsJ1Tl2bUehyDiSQaBPIDEzYiixltCnWR9eHP0iV31/FYXlxnN38THx9GnWhzC/MLpHdyfML4yY0Bjs2k6Hdzow7dxpPDb8sWOOn1+Wj1mZCfAOILsomwfmP8DXG78+4XNf0OEC/kg3nqAJ9w8nyCeIzhGd2ZW3i22Hth2z7dThU/H18sXPyw9fsy8B3gEE+wZTUF7AWyvfYlP2JjTH5qEWTVpgVmb2F+7HYrfwxIgnKLOVkVuSy9QRU2nuH8uHH8Izz0DbtrBoEYSE1OqfzCNJAheNxk8/wX33waFDYDZDfr6xPCYG7r8f+vaFrl0h4sR7n4BR4mwy7WjVwH8u/g8hviF0j+7O2n1rySvL45oe1xDkE0SJpYQmvk2w2q1sztlMZl4m4zqMw2xqmDFGN2VvoveHvbHajx0W75vLv+HqHldXu0+xpZjAlwJ5+dyXub3f7eSW5JJfls/0FdP5csOXgPHFVPWYX176JW1C2pBVkEW/Fv1oG9KWZxKeIbc0l7TDafiYfThccpj9hfvJOJLBy+e+zOqs1fya9itWu/WE+KrqFNGJK7tdSYR/BCPbjqRn0574mH2O2aakBFauhMWLjVdiorFs1Cj4z3+g/clryjyeJHDRaNlsMG8ePPEErK3SBD0wEFq0gHPOgWnTIDy8yj52G1sPbSXcP5xmQc0aPug6sGs7aYfSyC3NJbcklzJbGeM6jKvxhmdReRFB02q+mfrKmFc4UnoEPy8/ejXtRc+mPWkX1u6MYrTZbZTZyiixlFBsKaagvICDxQcpKCvgwk4X1rhfaSm89Ra88AIUFoJS0KsXjBwJl15qJHB1dt63PIYkcNHoaQ27dkFqKmzeDFlZ8NdfsG4dvPsu3H23qyNsGFa7leZvNOdg8UEApp83nZjQGNqFtaNHdI8G+/VwKgcOwLBhsH27US12++3GfFjYqfc925x2Z1ZCnC2UMqpRYmJg3Dhj2e+/w/jxYK35F/5Zx8vkReZ9mRRZigjwDiDA2z37yv70UyN5/9//wSWXuDoa93T23rYVohbi4sDf36gzHzfOqF9tDPy9/YkMiHTb5A0wwHiIlk8/hbIy18biriSBi0YtNRW6dTOm//jDqFPNzHRtTMIwZoxx03nOHJgyxagPF8eSBC4atXfegeRkGDEC+vSBG26Apk1dHZWo8OKLMHw4vPkmNGsGd9wBq1YZ9zOEJHDRyHVyDNhywQWwejV88UXt2oqLhuHvb7TxXrAALroIZs2CQYOMliiffOLq6FxPErho1J54Ai6/HB5/3EgQubmujkgcz2QyqlO++AL274cPPjDeb7/92GahjZG0QhGNWkgIfP89zJwJ99wD/frBc8/BlVdKSdzZrFY4cuTUr/x8KCgwXkVFYLcbD2n5+UFoqKuidw/SDlwIh8REmDQJNm2CyEh48kmYPLlxPChSX2w2I8F6H9f9SXk5JCQYD1QtXWq0wy8srPYQlUwm4ws2OBiaNDHeg4KMxN+9u/GF2+H0++LyKNIOXIhTGDoUNmyAP/+E11+He++FgweNErmo3uHDRv306tXGzcXkZOMR9zZtYOxYOO88SEoyHnc/dAh8fGDgQLj1VqMrg9DQE19hYcZ7UJB8eZ6KlMCFqIbWRh3rf/5jJPRzz3V1RO5n3jy4+mrIyzOqm/r0MZJzWBikpMD8+Ua1h8kEEyfCzTcbSd2/cY16Vi+kBC5ELZWVGSXvSMc4ENHRro3HHVksRvVSZKTRfr5vX6N0XVVhIWzcaHQc1tjrqp1FErho9IqL4auvjNL2li1GibLCuHHQs/ohGBstmw0eegjS0oyHbAYPrn67oCAYMqT6daJ+SAIXjVpBgVF6TE83Hqu/4QZo3twoWbZuLVUnxysuhquugl9/NTr/mjDB1RE1bpLARaP25ZdG8u7aFZYtM0qNomYffWQk74suMnpwFK4lD/KIRu2qq4yRelJTjbru0FCj6Zo7dWplt8O+fUZTPK2N+mdX6drVeJ87Fx54QB58cjVJ4KJRi4gw6r+XLTNaneTlHX1wxBW0NlpwTJ9uNKFTyhhVqEULo6WHyWTcLFTKGNTgxhuNp0j/+19Yvtxo1udM559vnGvMGHj7beP+wBNPGO26hQtorRvs1a9fPy2EO3v9da1B62ef1dpma7jzFhVp/fHHWvfqZZy/6mvqVK19fbUeOFDrDh2MZeefr/XIkVrHxGjt5XXs9lFRWg8frvW//qX1jz9qXVDgnJiXLNF67FitTSYjhnnznHMeoTWQpKvJqdIOXIgqysvhn/806sYnTIDvvnNuu+XiYuNBoY8/NkrPcXFw553GuVu1qt0xrFbYuRO2bj362rIF1q83fk34+BjDxk2YYDxY07Fj/T4gc+CAUbVisRhtw4cOrb9jC4MMqSZELWlt3KC75x4j2U2bZiS/+u4bJTvbqAKZP9/oUOuee4xubesruVosRtXQ3Lnwyy+wzTFYvI+P8aXUuvXREYrCw4333r2NAYKDg+t2rnnzjB4dBw2CFSvqJ35xlCRwIepowQK46y6jlUpIiHHD85VXzmxMxoICo+44KQl++81oUz1zJtx2W/3FXZO0NOPm7LZtxuPumZmQkWGU3vPzj25nMhm/BC6/3HjkvUWLkx+3rMz4QtAaliwx+u8W9UuexBSijsaONW7OLVwI//ufUc2RnW2M0Vjh4EFjva+v0eGS1karkTFjjERYwWaDGTOMB2Aq3HefUV1S0Se5s3XsaLyqc/Cg8UWVlWX0B/P330ZnXk8+afT6d9990KWL0Ube19eoasrONrp1nT3b+NyvvSbJu8FVVzFe9QX4AauA9cAm4FnH8m+BdY5XBrDuVMeSm5jCUy1ebNwgHDr02Jubt9564k1H0PqKK7TevPnodnfeaSwfNEjrp55q2Bukpys9XetRo6r/fFVfzZpp/c03ro727EYNNzFrUwIvA0ZrrQuVUt7AUqXU71rrqys2UEq9AeTVeAQhPFxoqFESTUw0qlN69jRexcUnbtu1K/z4o9HPeFyccVP0q6+M+uXExGNL5u6sfXujC1gwqlz27TNK6BaL0V1sVJQx/FxIiPQa6CqnTOCO7F/Rc6+341VZca6UUsBVwGhnBCiEO+jZE3bvNp5CXL3a6KTp22+NduMBAUbVwoEDRgdOcXHw119G1cKsWUa3tM2awbp10LKl0T/ILbfAxRe7+lPVnr8/tGtnvIT7qNVNTKWUGUgGOgDvaa0frbJuJDBdV1PB7lg/CZgE0KZNm367du2qj7iFcDmr1SihfvedcXMwMxNGj4bPPjNKp2BUMsTGwp49Rj14Vbt2Gf1mC3Eq9dIKRSkVCvwETNZapziWfQCka63fONX+0gpFNEY7dsD77xul9VatjCZ7XboYN0mFqI16aYWitT6ilEoALgBSlFJewGVAv3qJUoizULt2xgg/QtS3U95OUUpFOUreKKX8gTHAFsfqMcAWrfUep0UohBCiWrUpgTcHPnfUg5uA77TWvzjWXQP8z1nBCSGEqFltWqFsAPrUsO6W+g5ICCFE7XhIi1QhhBDHkwQuhBAeShK4EEJ4KEngQgjhoSSBCyGEh2rQ/sCVUjnAmTxLHwkcrKdw6pPEVTcSV924Y1zuGBOcvXG11VpHHb+wQRP4mVJKJdXU54orSVx1I3HVjTvG5Y4xQeOLS6pQhBDCQ0kCF0IID+VpCfwjVwdQA4mrbiSuunHHuNwxJmhkcXlUHbgQQoijPK0ELoQQwkESuBBCeCi3S+BKqV5KqeVKqY1KqblKqWDH8gil1N9KqUKl1Lsn2f8ZpdRepdQ6x2u8m8QVrpRaoJRKc7yHOTMux7opSql0pdRWpdT5NezfoNerDnHV+/VSSvVWSq1wfM4kpdRAx3IfpdRnjljXK6Xia9jfWdfqTONy1t9WTXF5K6U+d8SVqpSaUsP+DX29ahtXQ1+v66tcg3VKKbtSqnc1+9f9elU3VL0rX8BqYJRj+lbgecd0IDAcuAN49yT7PwM85IZxvQo85ph+DHjFyXF1A9YDvkAssB0wu8H1qm1c9X69gPnAOMf0eCDBMX038JljOhpj/FdTA16rM43LWX9bNcV1HfCNYzoAyABi3OB61TauBr1ex23TE9hRw/51vl5uVwIHOgOLHdMLgMsBtNZFWuulQKmHxnUJ8Llj+nNgojPjcpzvG611mdZ6J5AODKynczZEXM64Xhqo+CUQAmQ5prsBCwG01tnAEaAhHwY507ic9bdVU1waCFTGkIr+QDmQX0/nbIi4Gvp6VXUt9TgIjjsm8BTgYsf0lUDr0zjGv5VSG5RSn9bXz6N6iKup1nofgOM92slxtQR2V9luj2NZdRryetU2Lmdcr/uA15RSu4HXgYqf2OuBS5RSXkqpWIwxXmv693XGtTrTuJz1t1VTXN8DRcA+IBN4XWt9uIZjNOT1qm1cDX29qrqakyfwOl0vlyRwpdSfSqmUal6XYPzcvlsplQw0wfgWrYsPgPZAb4x/yDfcJK7TdppxqWoOVV2b0Ya+XrWN67ScIqY7gfu11q2B+4H/OHb7FOOLJAmYASQC1moO76xrdaZxnbbTjGsgYANaYFSDPaiUalfN4Rv6etU2rtN2mnFV7DsIKNZap9Rw+Lpfr/qun6rnuq5OwKrjlt3CSeqaj9s2Bkhxh7iArUBzx3RzYKsz48L49p9SZd08YIirr1dt43LG9QLyOPrsgwLya9guEejWUNfqTONy1t9WTXEB7wE3VtnuU+AqV1+v2sbV0Neryvo3gam1PFatrpfbVaEopaId7ybgCWBmHfdvXmX2Uoyf8i6PC5gD3OyYvhn42clxzQGuUUr5On5+dwRWVbN/Q1+vWsWFc65XFjDKMT0aSHPEGKCUCnRMjwWsWuvN1Xwmp1yrM40LJ/1t1RQXRvXEaGUIBAYDW47fuaGvV23jouGvV8X/gyuBb2ra+bSuV31889TnC7gX2OZ4vYzjG82xLgM4DBRi/LTs5lj+CdDfMf0FsBHYgPEP1dxN4orAuCGV5ngPb4C4Hsdo5bEVx91xN7letYmr3q8XRmuhZIy65ZVAP8fyGEcsqcCfGF13NuS1OtO4nPW3VVNcQcBsYBOwGXjYTa5XbeNq0OvlWBcPrKhmnzO6XvIovRBCeCi3q0IRQghRO5LAhRDCQ0kCF0IIDyUJXAghPJQkcCGE8FCSwIUQwkNJAhdCCA/1/+Lynh1H+5LkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  12812500 5918765 0.4134095626719308\n",
      "compare to Census 12812508 Leip has Trump + Biden = 5918806.0 0.4134095626719308\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 12812508\n",
    "atlasTrump = 2446891.\n",
    "atlasBiden = 3471915\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 12812500 12812500.0\n",
      "state Trumpers before, after slice opn are 2446850 2446850.0\n",
      "state Bideners before, after slice opn are 3471915 3471915.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 168 grids for 17 districts\n",
      "starting grid no 0 at x = -91.34566733333332, y = 37.85927192857142 \n",
      "starting grid no 5 at x = -91.34566733333332, y = 39.581201214285706 \n",
      "starting grid no 10 at x = -91.34566733333332, y = 41.303130499999995 \n",
      "starting grid no 15 at x = -91.01084399999999, y = 38.20365778571429 \n",
      "starting grid no 20 at x = -91.01084399999999, y = 39.925587071428566 \n",
      "starting grid no 25 at x = -91.01084399999999, y = 41.647516357142855 \n",
      "starting grid no 30 at x = -90.67602066666666, y = 38.548043642857145 \n",
      "starting grid no 35 at x = -90.67602066666666, y = 40.26997292857143 \n",
      "starting grid no 40 at x = -90.67602066666666, y = 41.99190221428571 \n",
      "starting grid no 45 at x = -90.34119733333333, y = 38.8924295 \n",
      "starting grid no 50 at x = -90.34119733333333, y = 40.61435878571429 \n",
      "starting grid no 55 at x = -90.34119733333333, y = 42.33628807142856 \n",
      "starting grid no 60 at x = -90.006374, y = 39.23681535714285 \n",
      "starting grid no 65 at x = -90.006374, y = 40.95874464285714 \n",
      "starting grid no 70 at x = -89.67155066666666, y = 37.85927192857142 \n",
      "starting grid no 75 at x = -89.67155066666666, y = 39.581201214285706 \n",
      "starting grid no 80 at x = -89.67155066666666, y = 41.303130499999995 \n",
      "starting grid no 85 at x = -89.33672733333334, y = 38.20365778571429 \n",
      "starting grid no 90 at x = -89.33672733333334, y = 39.925587071428566 \n",
      "starting grid no 95 at x = -89.33672733333334, y = 41.647516357142855 \n",
      "starting grid no 100 at x = -89.001904, y = 38.548043642857145 \n",
      "starting grid no 105 at x = -89.001904, y = 40.26997292857143 \n",
      "starting grid no 110 at x = -89.001904, y = 41.99190221428571 \n",
      "starting grid no 115 at x = -88.66708066666666, y = 38.8924295 \n",
      "starting grid no 120 at x = -88.66708066666666, y = 40.61435878571429 \n",
      "starting grid no 125 at x = -88.66708066666666, y = 42.33628807142856 \n",
      "starting grid no 130 at x = -88.33225733333333, y = 39.23681535714285 \n",
      "starting grid no 135 at x = -88.33225733333333, y = 40.95874464285714 \n",
      "starting grid no 140 at x = -87.99743399999998, y = 37.85927192857142 \n",
      "starting grid no 145 at x = -87.997434, y = 39.581201214285706 \n",
      "starting grid no 150 at x = -87.997434, y = 41.303130499999995 \n",
      "starting grid no 155 at x = -87.66261066666667, y = 38.20365778571429 \n",
      "starting grid no 160 at x = -87.66261066666667, y = 39.925587071428566 \n",
      "starting grid no 165 at x = -87.66261066666667, y = 41.647516357142855 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 766310.6343316813 21007057.884750243 145.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 17   #IL congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if tractGeom[t].type == clipPoly.type :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if vtdGeom[p].type == clipPoly.type :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f5a471b-867e-4db1-abdc-55f4b3d1f27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 0.00016990724566590647\n"
     ]
    }
   ],
   "source": [
    "#print(p,vtdGeom[p].area)  #this will spit out the precinct number where we choked (again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b538ed92-bf09-41ce-9758-8a764cbff950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigh, this precinct's geom is still whacked.  Convex-hull it\n",
    "# vtdGeom[2034] = vtdGeom[2034].convex_hull  #after this line, go back up and redo the grid code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.210250000041429e-07 2.407141499994983e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4 3.0 0 110.7 0.3766 205987.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4 4.0 0 110.7 0.352 205987.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4 5.0 0 110.7 0.329 205987.9\n",
      "I am working on tract number 20 of 3265 tracts\n",
      "I am working on tract number 40 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 43 4.0 2 90.0 1.1859 277416.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 43 9.0 0 102.1 1.2731 256003.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 50 4.0 3 90.0 1.3181 214315.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 50 5.0 3 90.0 1.1948 214315.7\n",
      "I am working on tract number 60 of 3265 tracts\n",
      "I am working on tract number 80 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 91 1 240109.30916910438 1.7847\n",
      "8 yoyos for tract,wedge,wedgePop,r= 91 1 73797.41191598834 0.8924\n",
      "9 yoyos for tract,wedge,wedgePop,r= 91 1 243803.63593385427 1.8956\n",
      "10 yoyos for tract,wedge,wedgePop,r= 91 1 112315.44877172355 1.394\n",
      "10 yoyos for tract,wedge,wedgePop,r= 91 1 189000.70452736545 1.6448\n",
      "11 yoyos for tract,wedge,wedgePop,r= 91 1 239351.22485895094 1.7702\n",
      "11 yoyos for tract,wedge,wedgePop,r= 91 1 226349.78193502294 1.7075\n",
      "12 yoyos for tract,wedge,wedgePop,r= 91 1 207928.04353580542 1.6761\n",
      "I am working on tract number 100 of 3265 tracts\n",
      "I am working on tract number 120 of 3265 tracts\n",
      "I am working on tract number 140 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 141 3 256662.50300484712 1.6891\n",
      "8 yoyos for tract,wedge,wedgePop,r= 141 3 214888.13766677375 0.8445\n",
      "9 yoyos for tract,wedge,wedgePop,r= 141 3 279285.99521047104 1.8292\n",
      "10 yoyos for tract,wedge,wedgePop,r= 141 3 223769.56242654694 1.3369\n",
      "10 yoyos for tract,wedge,wedgePop,r= 141 3 228520.56367809616 1.583\n",
      "11 yoyos for tract,wedge,wedgePop,r= 141 3 264059.2345403967 1.7061\n",
      "I am working on tract number 160 of 3265 tracts\n",
      "I am working on tract number 180 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 181\n",
      "I am working on tract number 200 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 217 4.0 0 130.5 0.8694 225006.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 217 5.0 0 130.5 0.7587 225006.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 217 6.0 0 130.5 0.662 225006.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 217 7.0 0 130.5 0.5777 225006.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 217 8.0 0 130.5 0.5041 225006.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 217 3 321168.9450014014 1.8863\n",
      "8 yoyos for tract,wedge,wedgePop,r= 217 3 41806.17563147354 0.9431\n",
      "9 yoyos for tract,wedge,wedgePop,r= 217 3 319013.5265471668 1.8669\n",
      "9 yoyos for tract,wedge,wedgePop,r= 217 3 259023.1505197422 1.405\n",
      "10 yoyos for tract,wedge,wedgePop,r= 217 3 71818.35012600513 1.1741\n",
      "10 yoyos for tract,wedge,wedgePop,r= 217 3 86404.62964225629 1.2895\n",
      "10 yoyos for tract,wedge,wedgePop,r= 217 3 145716.6723338127 1.3473\n",
      "11 yoyos for tract,wedge,wedgePop,r= 217 3 227660.69154959335 1.3761\n",
      "12 yoyos for tract,wedge,wedgePop,r= 217 3 191983.4504095753 1.3617\n",
      "13 yoyos for tract,wedge,wedgePop,r= 217 3 214989.9842984343 1.3689\n",
      "13 yoyos for tract,wedge,wedgePop,r= 217 3 205312.72715619332 1.3653\n",
      "loop31.0, tr217,wedgePops756048.84, 164688.0, 195852.8, 196073.4, 199434.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?01113 \n",
      "   targetWP, latest drx4 are tWP,dr, 196329.5,-0.0642, 196329.5,0.0006, 196329.5,0.0016, 196329.5,-0.0018\n",
      "I am working on tract number 220 of 3265 tracts\n",
      "I am working on tract number 240 of 3265 tracts\n",
      "I am working on tract number 260 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 266 3 229860.90080465048 1.4794\n",
      "8 yoyos for tract,wedge,wedgePop,r= 266 3 32655.56739799917 0.7397\n",
      "9 yoyos for tract,wedge,wedgePop,r= 266 3 256291.3975097803 1.4948\n",
      "10 yoyos for tract,wedge,wedgePop,r= 266 3 55005.50842234344 1.1173\n",
      "10 yoyos for tract,wedge,wedgePop,r= 266 3 81358.26583801048 1.3061\n",
      "10 yoyos for tract,wedge,wedgePop,r= 266 3 120483.54082655907 1.4004\n",
      "10 yoyos for tract,wedge,wedgePop,r= 266 3 148927.11500795736 1.4476\n",
      "10 yoyos for tract,wedge,wedgePop,r= 266 3 198532.93707659218 1.4712\n",
      "11 yoyos for tract,wedge,wedgePop,r= 266 3 237005.6413327776 1.483\n",
      "I am working on tract number 280 of 3265 tracts\n",
      "I am working on tract number 300 of 3265 tracts\n",
      "I am working on tract number 320 of 3265 tracts\n",
      "I am working on tract number 340 of 3265 tracts\n",
      "I am working on tract number 360 of 3265 tracts\n",
      "I am working on tract number 380 of 3265 tracts\n",
      "I am working on tract number 400 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 402\n",
      "I am working on tract number 420 of 3265 tracts\n",
      "I am working on tract number 440 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 11.0 0 85.4 2.3742 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 16.0 0 85.4 2.7641 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 17.0 0 85.4 2.6684 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 18.0 0 85.4 2.576 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 19.0 0 85.4 2.4869 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 20.0 0 85.4 2.4008 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 25.0 0 85.4 3.184 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 26.0 0 85.4 3.0738 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 27.0 0 85.4 2.9674 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 28.0 0 85.4 2.8647 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 29.0 0 85.4 2.7656 211397.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 30.0 0 85.4 2.6699 211397.5\n",
      "loop31.0, tr458,wedgePops764069.58, 211397.5, 148414.1, 202232.3, 202025.7, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 201754.1,-0.0924, 201754.1,0.3948, 201754.1,-0.007, 201754.1,-0.004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 31.0 0 85.4 2.5774 211397.5\n",
      "loop32.0, tr458,wedgePops764069.58, 211397.5, 148414.1, 202232.3, 202025.7, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 201754.1,-0.0892, 201754.1,0.3948, 201754.1,-0.007, 201754.1,-0.004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 32.0 0 85.4 2.4882 211397.5\n",
      "loop33.0, tr458,wedgePops764069.58, 211397.5, 148414.1, 202232.3, 202025.7, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 201754.1,-0.0861, 201754.1,0.3948, 201754.1,-0.007, 201754.1,-0.004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 458 33.0 0 85.4 2.4021 211397.5\n",
      "loop34.0, tr458,wedgePops764056.26, 211384.2, 148414.1, 202232.3, 202025.7, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 201754.1,-0.0831, 201754.1,0.3948, 201754.1,-0.007, 201754.1,-0.004\n",
      "loop35.0, tr458,wedgePops702026.86, 149354.8, 148414.1, 202232.3, 202025.7, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?5011 \n",
      "   targetWP, latest drx4 are tWP,dr, 201754.1,-1.1595, 201754.1,0.3948, 201754.1,-0.007, 201754.1,-0.004\n",
      "loop36.0, tr458,wedgePops712926.68, 160254.6, 148414.1, 202232.3, 202025.7, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?6011 \n",
      "   targetWP, latest drx4 are tWP,dr, 201754.1,0.8162, 201754.1,0.3948, 201754.1,-0.007, 201754.1,-0.004\n",
      "loop37.0, tr458,wedgePops764069.58, 211397.5, 148414.1, 202232.3, 202025.7, Overedge?0, 1, 0, 0, ,Satisfied?0011,yoyo?6011 \n",
      "   targetWP, latest drx4 are tWP,dr, 201754.1,1.3747, 201754.1,0.3948, 201754.1,-0.007, 201754.1,-0.004\n",
      "7 yoyos for tract,wedge,wedgePop,r= 458 0 211397.54733449058 3.3505\n",
      "loop38.0, tr458,wedgePops706010.91, 153338.9, 148414.1, 202232.3, 202025.7, Overedge?1, 1, 0, 0, ,Satisfied?0011,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 225961.8,-1.6752, 225961.8,0.3948, 225961.8,-0.007, 225961.8,-0.004\n",
      "loop39.0, tr458,wedgePops741870.94, 153338.9, 148414.1, 220976.9, 219141.1, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0011 \n",
      "   targetWP, latest drx4 are tWP,dr, 225961.8,-1.6752, 225961.8,0.3948, 225961.8,0.0653, 225961.8,0.0235\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.67523 211397.5473 153338.8731 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.21344 105268.4382 148414.0668 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.28721 202232.3026 220976.9172 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.49755 202025.6645 219141.0861 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAx00lEQVR4nO3dd3xV9f3H8dc3N3ux94qsMJKAihXrABEBQZmiILgoWlddtY5q66xFq/1pq61axKoEUYEgorhFtE7IhXsv3JsQ9gqEEbLn/f7+yE0IGMwNufeee24+z8cjj7vv/fRg3+ec7zpKa40QQgjzCTO6ACGEEKdGAlwIIUxKAlwIIUxKAlwIIUxKAlwIIUwqPJA/1r59e52UlBTInxRCCNNbt27dQa11hxOfD2iAJyUlsXbt2kD+pBBCmJ5SakdDz0sTihBCmJQEuBBCmJQEuBBCmJQEuBBCmJQEuBBCmJQEuBBCmJQEuBBCmJQEeDPt2rudJR8spLKq0uhShBAtjNcBrpSyKKWsSqmVnsfTlVIblVJupdQw/5UY3J5/9a/c8fAcxlw1jO8zvza6HCFEC9KUI/A7AGe9xw5gKrDGpxWZzAbnOvr06k9pWSnTbryI2/98PXmH9htdlhCiBfAqwJVS3YEJwPza57TWTq11lr8KM4Oy8jKyt2ziklGT+fKd9dw+535WfPIOF0xL4b/vvkR1dbXRJQohQpi3R+DPAfcC7qb+gFLqRqXUWqXU2ry8vKZ+PKi5chxUVVeRmnw6MdGx3HfLY3y2OJO0QWfy4FO3c+l157J+o6z9IoTwj0YDXCl1KXBAa73uVH5Aa/2K1nqY1npYhw4/W0zL1OwuKwBpA8+oe65vUjKLX1zFv/7yJrkH9nLpdefywLzfkV9wxKgyhRAhypsj8HOBiUqp7cBiYJRSaqFfqzIJuyuT1olt6NE16bjnlVJMGnslXy21M2fGbSxc9h8umJbCuyvfRC4iLYTwlUYDXGv9gNa6u9Y6CZgBfKG1nu33ykzA7rKSMuB0lFINvp4Y34rHfv8sq978nqQefbjzkd8w7caLcOU4AlypECIUnfI4cKXUFKXUbuAc4AOl1Me+Kyv4VVRW4MpxkDbg9Ebfm5I8lOXzV/PMQy+TtXUTY2adxePP309xSVEAKhVChKomBbjWerXW+lLP/QzPkXmU1rqT1nqsf0oMTllbNlJRWUGKFwEOEBYWxszJ1/P1UgdXXnYtL735d0ZMT+ODz5dJs4oQ4pTITMxT1FAHpjfatm7P3x56ifcWrKFNq7bceN8Mrr5jItt25fijTCFECJMAP0U2ZyYJcYn06tb7lD4/LG04q974nkd//yw/bfiWi648nb+/8jhl5WU+rlQIEaokwE+Rw9OBGRZ26pswPDycuTN/x1dL7IwbOYlnX3mci648nS+/bVHdCUKIUyQBfgoqqyrZtNlGqpft343p3KEr/3pyIW+9uIqwsDBm334ZN9x7JXtyd/nk+4UQoUkC/BRs3uakvKKctIG+CfBaF5x9EZ8tzuS+Wx7ji/+tYuT0NF568++y0qEQokES4KfA7qzpwPTVEXh9UZFR3D7nfr58ZwO/HjaCx5+/n7FXncUP1m98/ltCCHOTAD8FdpeVuNh4evfs77ff6NntNF7/v+W89uxSikqKmHrDKO585DccPHzAb78phDAXCfBTYHNlkpI8tFkdmN4aM+IyVr+7gduuv5flHy3mgmkpvLHkFVnpUAghAd5UVVVVbMza4PUEHl+IjYnjgVuf4NO31jI4eSgPzLuNidefj82ZGbAahBDBRwK8ibbsyKKsvNSrKfS+1u+0gbzz74954YnX2bN/F+OvOYcHn7qDo4X5Aa9FCGE8CfAmqj3q9UcHpjeUUkwZN5M1Sx1cf8UtvLH0ZS6YlsKSDxbKlHwhWhgJ8CayZ60nOiqGvkkDDK0jMb4Vj//h/1j1xnf06JrEHQ/PYfpvLyZry0ZD6xJCBI4EeBPZnZkMTh6CxWIxuhQAUgaczooFa3j6wX/jzLEz5qqz+Ms/HpCVDoVoASTAm8DtduPIWk/agKYtYOVvYWFhzJryG9YsdXD5hNn8641nGTl9CKu+XC7NKkKEMAnwJti6M5uS0mLD2r8b065NB5798yssn7+aVgmtmPuHK7jmzsns2L3V6NKEEH4gAd4EtUvIpvp4Cr2vnTX013y08Ecevutv/GD9mlFXDuX/5v9FVjoUIsRIgDeBzWklOiqa/qcNMrqURoWHh3PjrDv4aomdMRdcxjMvPcromWfw1fefGl2aEMJHJMCbwO7MZGDfVMLDw40uxWtdOnbj339N560XPgTgqtsm8Nv7Z7LvwB6DKxNCNJcEuJdqOzBTm3gFnmBxwfDRfL7Yyh9ueoTPvv6AEZen8vLC52SlQyFMTALcS9t3b6GwuCBoOzC9ERUZxZ1z/8gXb6/n7NPP57Hn7uWS2Wfz0/pvjS5NCHEKJMC9VHcNTBMHeK1e3XvzxnPLefWZdzlaeJTJc0dy96M3cOhIntGlCSGaQALcS3ZnJpERkfTvM9joUnxCKcW4kZP4aomNW6+9h6UfpnPBtBQWLpuP2+02ujwhhBckwL1kz1rPgL4pREZEGl2KT8XGxPHH3z3Jp2+tZWC/NO578hYmXn9+3RmHECJ4SYB7QWuNw2U1dft3Y/r3HsS7L33CPx57jV37djD+mnN46Ok7ZaVDIYKYBLgXdu3dTn7BEVKDbAq9rymlmDZ+FmuWOrj28pt4fclLjLg8lWWrFsmUfCGCkAS4F2qXkE0z6RDCpmqV0Jon7n2OD17/lm6devC7P13H9JvGsHmb0+jShBD1SIB7wZ5lJdwSTnKIdGB6K23gGax47WvmPfAim7I3MHrGmfz1hQcpKS02ujQhBBLgXrE7rST3GUx0VLTRpQScxWLh6mk3sGapg6njr+KF//6NkdOH8PHqFdKsIoTBJMAbobXG5swM6Q5Mb7Rv25H/e3g+y/7zBQlxCcy553Kuu3sKO/dsM7o0IVosCfBG7N2/iyNHD5l2Cr2vnX36eXyU/iN/uvMpvlu3hguvGMJz85+kvKLc6NKEaHEkwBth9DUwg1FEeAQ3zb6L1e/aGH3+BP720iOMnnEGa3743OjShGhRJMAbYXdZsVgsDOqXZnQpQadrp+68PO8t0v+5ErfWzLz1Em5+YBa5eXuNLk2IFkECvBF2p5V+SQOIiY4xupSgNfKcMXy+OJN7fvtnPv5qBSMuT+U/i/5BVVWV0aUJEdIkwH+B1hqbK1Pav70QHRXNXTc8xBfvrOesIb/mkb/fw7irz+anDd8ZXZoQIUsC/Bfk5u3l4OEDQXcR42CW1L0Pbz6/gv88/Tb5BUeY/JsR/P6xGzmcf9Do0oQIOV4HuFLKopSyKqVWeh63VUp9qpTa7Llt478yjVF3DUzpwGwSpRTjR03hq3dt3Hz13Sz5YCHnT0th0fIFstKhED7UlCPwO4D6c6nvBz7XWvcDPvc8Dil2ZyZKKQYnDzG6FFOKi43noTvm8cminxjQZzB/eOImJv1mBA5Z6VAIn/AqwJVS3YEJwPx6T08CXvfcfx2Y7NPKgoDNlUnfpGRiY+KMLsXUkvsMZsnLn/H8owvYsXsrl1xzDn9+5m4Kio4aXZoQpubtEfhzwL1A/fPfTlrrfQCe244NfVApdaNSaq1Sam1enrmu+OJwrQ/5FQgDRSnF5RNms2apg6un3ciCt19kxLRUln+0WKbkC3GKGg1wpdSlwAGt9bpT+QGt9Sta62Fa62EdOnQ4la8wxIGDueTm7ZX2bx9rndiGJ+/7Bx+8/i1dOnXj1oeu4cpbxpGz3WV0aUKYjjdH4OcCE5VS24HFwCil1EJgv1KqC4Dn9oDfqjRA3TUwZQihXwwZdCbvv/YNT97/T+zOTEbPOJN5L/6J0rISo0sTwjQaDXCt9QNa6+5a6yRgBvCF1no2sAK41vO2a4H3/FalAWyumin0g/tLB6a/WCwWrr38t6xZ6mDS2Cv552tPMXL6ED5Zs9Lo0oQwheaMA58HXKyU2gxc7HkcMhwuK7179iMhPtHoUkJeh3adeP7RBSx95XPiYuK4/u6pXHfXFHbt3W50aUIEtSYFuNZ6tdb6Us/9Q1rri7TW/Ty3h/1TojFsTqs0nwTY8DPO5+NFP/GnO+bxv7WrGTl9CP9YME9WOhTiJGQmZgMOHclj7/5d0oFpgIjwCG66+m5Wv2tj1LnjeOpff+bimWfy9Y9fGF2aEEFHArwBdTMw5QjcMN069+A/T7/Nm8+voKqqihm3jKPbsEgJciHqkQBvQO0a4CnJQ40tRDDq3HF8/raV8RdOBuD5V/9qbEFCBBEJ8AbYXVaSuvehVUJro0sRQEx0DKMvuBSAv9z7nLHFCBFEJMAbYHdZSRkw1OgyRD0Ol5XYmDj6Jg0wuhQhgoYE+AmOHD3Mrr3bZQnZIGNzZjK4/xAsFovRpQgRNCTATyBLyAaf6upqNmZvkGGdQpxAAvwEtUudpkiAB40tO7IoLSuRnaoQJ5AAP4HNlUmPrkm0bd3O6FKEh5wVCdEwCfAT2F3rSZXhg0HF5rQSHRUjHZhCnEACvJ6CoqNs35UjE3iCjN2VyaD+aYSHhxtdihBBRQK8HjlVDz5utxtH1noZFSREAyTA65EADz5bd26muKRI/k2EaIAEeD12ZyZdOnWnfdsGrw4nDOCoW5dGAlyIE0mA12N3WUmTI72gYnNZiYqMon/vQUaXIkTQkQD3KCouZOvOzXKqHmTsLisD+6YSER5hdClCBB0JcI+N2RvQWssIlCCitcbhssq/iRAnIQHuUbuErIx2CB479myloOionBUJcRIS4B52l5VO7bvQsX1no0sRHnU7VTkCF6JBEuAedlemjHQIMnZnJhHhEdKBKcRJSIADJaXF5GzPklP1IGN3rWdA3xSiIqOMLkWIoCQBDmzMtuF2u6X9O4horWvOimSnKsRJSYBTc6oOsoRsMNm9bwf5BUdIlZ2qECclAQ7Ys6y0b9uRLh27GV2K8KjtwJQjcGFWbrfm6x+3cdMDGQwd+zw7dh/x+W/I8m7UHIGnDjgdpZTRpQgPu8tKuCWcgf1SjS5FiCYpLatkweK1LFq+nu31Qruq2u3z32rxR+ClZaVkb3PKkV6Qsbus9O8ziOioaKNLEaJJXnz9O5584Uu6dEzghScmceVlacTHRXJaj7Y+/60WH+DOHDvV1dUy1jiI1HRgWklNlp2qMJ+1G3aTktyJJa/MZsq4wWRvO0hqcmfCwnx/ht/iA1yWkA0+e/fv5tCRPNmpCtPRWmPP2k/awC4AVFW5cW4+QMoA/0wQlAB3ZtKmVTu6de5pdCnCw+6SUUHCnHbvO0r+0VJSBnQCYPP2g5SVV5E2UALcL2zSgRl07C4rYWFhDO6fZnQpQjSJ3ZULQNqAmiNwm7PmcaocgfteeUU5WVs2kiZT6IOKzWmlX9IAYqJjjS5FiCaxOXOxWBQD+nYAwO7cR2xMBL17+r4DE1p4gLtyHFRVV8mpepCRJWSFWTmycunfuwMx0TXr19td+xncvxMWi3+itkUHeG0HpkyhDx65eXs5cChXOpWF6WitsTlz65pLqqvdbMze77f2b2jhAW5zZtIqoTU9u51mdCnCo26nKkfgwmT2HSjk0JES0jwBvmXHIUrLKv02AgVaeIA7sqykSAdmULE7M1FKMbj/EKNLEaJJ7LUdlp4j7toOzDQjA1wpFa2U+lEptUEptVEp9ajn+SFKqe+UUnal1PtKqUS/VekHFZUVODfb5SLGQcbmzKRPr/7ExcYbXYoQTWJz7SMsTDG4f80QQrsrl+iocPomtffbb3pzBF4OjNJaDwGGAuOUUsOB+cD9WutUIAP4g9+q9IPsrZuoqKyQttYgY89aL80nwpTsrv30S2pXrwMzl0H9OxEe7r+Gjka/Wdco8jyM8PxpIBlY43n+U2CaXyr0E7vTMwNTwiJo5B3aT+6BPaTIFHphQnbnvrr2brdb48ja79fmE/CyDVwpZVFKrQcOAJ9qrX8AHMBEz1umAz1O8tkblVJrlVJr8/LyfFCyb9izrMTHJZDUvY/RpQgP6cAUZrX/YBEHDhXXjTjZuvMwxSUVfpvAU8urANdaV2uthwLdgV8ppVKAOcCtSql1QAJQcZLPvqK1Hqa1HtahQwcfld18NmcmqcmnExbWovtxg0rtGuCDk6UDU5iLzbkPODbj0uE6vkPTX5q0HrjWOl8ptRoYp7V+BhgDoJTqD0zwfXn+UVVVhXOzjaun/dboUkQ9jiwrp/XsS2J8K6NLCTo/WI8y45aNpAyIZ0CfWOLjLCTGW4iPC/fcWkiICych3uL5CychzkJMdJiMsgoAhysXpajrwLS5comKtNC/t/86MMGLAFdKdQAqPeEdA4wGnlJKddRaH1BKhQEPAS/5tVIf2rzdSVl5mUyhDzI2p5UzU882uoygVFbupqJSk2kvJNNeeErf0SrBQtdOUXTuGEmrhHDPTuDEWwvx8fV2BnE1O4OoSDlT/SU2Zy69e7YlPi7K83gfA/t2JCLc4tff9eYIvAvwulLKQk2Tyzta65VKqTuUUrd63rMMeM1fRfqazSlLyAabw/kH2ZO7k+uvuNnoUoLSiOFt+OH9M1m4bD+H8yspLKomv6CK3Lxy9u6voKi4utHvOFpYzdHCEpw5Jc2qpUO7CLp2iqJjuwgSE2qO9OPjw0mMqwn/2p1BbfjX3NbsFMLDQ/NswJ6Vy/DTa1Y0dbs1Dtd+Jo8b5PffbTTAtdY24GdJp7V+HnjeH0X5m8NlJTYmjt49+xtdivCoW5ddOjBPqnuXaO6/tdcvvkdrTXFJNYXF1RQW1dwWFVdRUFRNUVE1BUVVFBUfuy0srubwkUr27q9g7/5yKqt0o3XkHaok71Bls/63KAVdO0XRtVMk7dtGHBf0EeFhaK254aquxMcF/1UfDx4uZt/+wrr27x17jlBYXO73DkxoodfEtDkzSUkeisXi39Mb4b3aDsyU5KHGFmJySini48KJjwunS8dT/57qak1RSTWFRVXHdgZFVT/fKXh2BoVF1Rw4VMG+/RXk5jU4nuE4WsOe3HL25Jaf9D3JfeIYP6rdqf+PCJATl4ytW1LWc1EHf2pxAV5dXc3G7A1cNXmO0aWIeuwuK7269aZ1YhujSxGAxaJolRBOq4TmRURFpfvYEX9RtSf0q447Qzi2k6gi46ODALw8L5lLLvTPEqy+VhvYtRdxsDtziQgP83sHJrTAAN+yI4vSshJp/w4ydpdV/k1CUGREGG1bh9G2dYRX73/hiWQ/V+R7dtc+knq0ITE+2vM4lwF9OxIV6f94bXFdy3INzOCTX3CEnXu2yQQeYUp217EZlzUX5M4NSPs3tMAAtzmtREfF0DdpgNGlCA/ZqQqzOpxfwu59R+sCe/e+o+QXlEmA+4vdlcmg/mmEh7e41qOg5ZAAFyblcO0Hfr6ErAS4H7jdbhxZ6+UKPEHG5sykW+eetG3t/04fIXzJ5jp+Cr3dlUu4JYyB/ZoxBKgJWlSAb925meKSIjnSCzL2LKvMihWmZHfl0rNba1onxtQ97t+nPdFRgTnDb1EBXneqLmERNAqKjrJtZw6pclYkTMhe7xqYNdfE3EdqcmCaT6CFBbjNZSUqMor+vf0/xVV4x+FaD0j7tzCf/IJSduzJrwvwvfsLOZxf6teLGJ+oRQW43WVlYN9UIsK9G5Mq/E9GoAizcmTVdGDWBrbd0x7uz4sYn6jFBLjWGofLKmttBBm7K5POHbvRoV0no0sRoknsDUyhr39NzEBoMQG+Y89WCoqOypFekLG7rHJhaWFKdlcu3Ton0rZ1LFAzhLD+NTEDocUEeO1iSTLbL3gUFReyZUe27FSFKdlOmHHpcOWSGoAFrOprMQFud2YSER4hHZhBZNNmG1pradYSplNYVM62nYfrAjw3r/C4a2IGSssJcNd6BvRNISoyyuhShEftWZEcgQuzcWQdv2Rs7YqEgZqBWatFBHjNAjOZEhRBxu6y0rFdZzp36Gp0KUI0ib12Cn29JWTrXxMzUFpEgO/et4P8giMyWSTI2J2Z0nwiTMnu2kfnjgl0aBcP1HRg9unVjrjYyIDW0SICXDowg09JaTGbt7tIHTDU6FKEaDKbM/e4GZf2rNyAt39DCwlwu8tKuCWcAX1TjC5FeGzabMftdsvCYsJ0iksq2LLjUF1g5x0qIvdAISkBnEJfq8UEeP8+g4iOija6FOFhr70GpvRLCJPZmL0frY/NuKxtD5cjcD+o6cC0kposQRFM7C4r7dp0oGun7kaXIkSTHLtoce0a4DVT6AcnB342ccgH+N79uzl0JE/av4OMzTMqSClldClCNInNmUuHdnF0al/TgenIyuW0nm3rrokZSCEf4HaXnKoHm7LyMrK3bpJhncKUHJ4ZmLUHHzZnbt01MQOtBQS4lbCwMAb3TzO6FOHh3GynurpaAlyYTmlZJdnbDtY1nxzOL2FPbkHAJ/DUCvkAtzmt9EsaQEx0rNGlCA+bS4Z1CnPatPkAbrc+bgVCOHZNzEAL+QCXJWSDj8NlpXWrtnTv0svoUoRoEruz9hqYx0+hTzGgAxNCPMBz8/Zy4FCuHOkFGZszk9Rk6cAU5mNz5tK2dQxdOyXUPe5V75qYgRbSAS5Xewk+5RXlZG3ZKBcxFqZUM+OyS93Bh92VG9Ar8JwotAPcmYlSisH9hxhdivDI2rKRyqpKGRUkTKesvIrsLQfrAju/oJSde/INmcBTK7QD3GWlT6/+xMXGG12K8Kg9K5JmLWE2rpwDVFW764YM1k3oGRDYizjUF9IBbnNZJSiCjM2ZSWJ8K3p16210KUI0ic15/AxMh2cKfcoA467nGrIBnndoP7kH9pAiU+iDisNlJUVmYAoTcmTl0joxmu5dWgE1U+i7d2lVd01MI4RsgMupevCprKrEmWMPmX8Tt9tNfkGh0WWIALE5j5+Bac/aX3dBB6OEN/YGpVQ0sAaI8rx/idb6YaXUUOAlIBqoAm7RWv/ox1qbpHYN8MHJ0oEZLLK2bKS8opzU5KFGl9IslVVVfLrme2649zEAlFL06NqJ+LhYEuPjjrtNiIsjIf7427rX44+9LzIicFcyF01XUVmNK+cAN1z1KwAKisrYtvMw0yekGlpXowEOlAOjtNZFSqkI4Bul1CrgMeBRrfUqpdR44GlgpP9KbRpHlpXTevYlMb6V0aUID4drPYBpJ1bt2L2PRe+t4u0VH5N36Ejd85PHjkSpMIqKSygsLiH3wCFytu2ioKiYwqJiKquqGv3u6KhI4uNiG9wJNLwz8NyPjyPB87mEuFgsFos/N0GLlbUlj8oqd92My41ZtZdUM24ECngR4FprDRR5HkZ4/rTnL9HzfCtgrz8KPFU2p5VhacONLkPUY3NlEh+XwGk9+hpditcqKiv55KvvSM/4kDU/ZBIWFsbo885m1tTxXHjOMK8Cs6y8gqLiYgqLSygsqndbdOxxUXEJBUXFx93u2L3vuNfcbnejvxUbE10v8GsCvsGdQXxcXfifeBsbE01YWMi2rp4SW90MzNolZGsvYhzkTSgASikLsA7oC7yotf5BKXUn8LFS6hlq2tJ/7bcqm+hw/kH25O7k+ituNroUUY/dZSUleagpwmHbrj28tfwj3n7/Yw4ezqdb547cc9M1zJg4ji4d2zfpu6KjIomOiqR92zanXI/WmtKyMgqKSigqLq4J+aKSn+8Uin++U8jNO1S3UygqLmn0t5RSdUf1xwK/3hnAcTuDhncCCfFxREdFhkxntd2VS2J8FEnda/4NHVm5x10T0yheBbjWuhoYqpRqDWQopVKAG4G7tNZLlVJXAK8Co0/8rFLqRs976dmzp6/q/kV1MzBNeqoeiqqqqtiUbWP21BuMLuWkyisq+Gj1tyxavopvfrRisYRx8fnDmTVlPCOGn2lo84RSitiYGGJjYqBDu1P+nurqaopLyzxnAD8P/tpmoBNfO5xfwI7duXU7hbLy8kZ/K9xiOa6d38z9A3ZnLoOTOwXFErL1eRXgtbTW+Uqp1cA44FrgDs9L7wLzT/KZV4BXAIYNG6ZPudImqA3wFJN3loWSnO0uyspLg3IK/ZYdu1m0/EPeef9TDucfpUfXTtx783VcOXEsnZsRlsHIYrGQGB9HYnxcs76nsqqKwrqzgZKGm4jqNRUVee7nHjjE5q07617zVf9AUo9uTLvkIsLDfb+TrayqxplzgOuuGAZAUXE5W3YcYvLYQT7/rabyZhRKB6DSE94x1BxlP0VNm/cIYDUwCtjsxzqbxObMpFe33rROPPVTVuFbtUvIBsu6NOUVFaz64n8szPiQ79ZtINxiYcyIc5g9dTzn/+oMUzTzGCkiPJy2rRNp2zqx8TefhNaa8orK43YCx/UD/EITUW3/QO1zbreb/yxaxl/v+x1nDR3sw/+lkL31IOUV1XVH3Js2HzjumphG8uYIvAvwuqcdPAx4R2u9UimVDzyvlAoHyvA0kwQDh2s9KQOGGl2GqMfhWk9sTBx9eiUbWkfO9p2kZ6zi3ZWfcuRoAb26deGB2+ZwxaVj6Ni+raG1tTRKKZ/1D6z68n88/Oy/mTz3LmZMHMuDt8+lbWvfjEBz1C4ZO+D4a2CmDTRuCn0tb0ah2ICfHTZprb8BzvRHUc2RX3CEHXu2ctWUOUaXIuqxOTMZ3H+IIe3IZeUVfPjF16RnfMj3mXbCLRbGXXgus6aM57yzzNGpKk5OKcX4UecxYviZPDc/nVfSl/LRV9/ywK1zuGryJc3+97U5c4mLjaR3z5odvN21n471rolppCa1gZuBLCEbfKqrq3FkrWfmpOsD+rvZW3eQnvEhSz78jPyjhST16MqDv5vL9EsvpkM7aV4LNXGxMTx4+1wunzCaPz71T+578nkWr/iYefffTsqAUx+6as/KJSW5E2FhnhmYzn2kBsHRN4RggDvqAlxGoASLrTuzKS0rITVAHZh5h47w2/sf5wero+65i847m7EjziExIZ6N2Vt+1hEmY59DR3KfJJa8/AxLP/ycx59/hUuuuY3rpl/GH26+rsmdt1VVbjZm7Wf21Jr/dkvLKtm8/RCXjDK2KbBWyAW4zZlJ9y69aNs6tEYPmFntsgaBOisqKS2jpLSMzh3b1419/vybH/j8mx9O+pn6Y5/rxjM3MPa5bhZkg2OfY4mOigqZsc9mppTi8gmjufiC4Tz9r//y2jsrWPnZ1/z5rhuZPPZCr/+NcrYfpKy8qm4Cz8bs/bjd2tAlZOsLuQC3Z1lJlQ7MoGJ3rSc6Kpp+SQMD8nu9unfho4X/qnvszdjnggbGQNeOfS4srhkK15yxz8eC3jxjn0NBq4R4/nLfbVxx2Rj+OO+f3PbQPBYtX8WT9/2Ofqc1Pi/F7lky9tgSssZexPhEIRXgBUVH2bYzh+kTrja6FFGP3ZXJwH5phIcb85+bP8c+NxT8DY59LvL92GdZG8V7Qwb1Z8Vrz5GesYp5Ly7g4pk38durL+eOOTNrJkedhN21j5joCPr0qjmjtzlzadcmli4dEwJV+i8KqQDfmLUBkA7MYOJ2u3FkrWfa+FlGl9JsRo59rr82Sv2xz405bm2UeuHe4E7hJE1DcbExIdEsZLFYuObySxk/6lye+Md8XnhtMRmrvuDxe25h7MiGVwKxu3IZ3L8TFktN/0jNNTE7B832CKkAD3Rbq2jctl05FBUXkioX1gB8O/a5pLTME+7H1kY5cUGshnYGvlgb5fhFseq/FkdifKzn9vjmo2BZG6V92zY898gfmDFxLH+c90/m3PMIF58/nMfuuZme3Y61bVdXu3Fk7WfGxJolqWuviXnRucGzGFtIBbgjy0rnjt3o0M7YFcLEMXWjgoJwCr2ZKaWIi40hLtaYtVEO5R9l++59TV4b5cTO4ZPuFBrqMPY0EUX4qClu+BlpfLzo37y6eDnPvvwGF15xA7fPuYqbrr6cqMhItu06TElpZd0EHudmzzUxg6T9G0IswG3OTNLk6Duo2FxWIiMiSe7j2+nNwjfMvDbKyUYD/XynEFfXSVy3xG5sDBaLhYjwcG6afTkTLx7BI39/iaf//V+WfPAZf7nvNg4ejgSOdWDWXsTY6DXA6wuZAC8qLmTLjmwmjbnC6FJEPTZnJgP7pRIRLqMqQpkv+gfg2NrpP+skLjrJzqAZ/QNxsTHHrZmeEBdLx3Zt2bpzNzNvvZ8O7UYC0C+pZvlguyuX1q1i6q6JGQxCJsA3bbahtZYlZIOI1hqHy8pE2akKL/lz7fRf6heovW3dKoEwSxiFRSXkHar5vtfeWcv1VwzD7qpZQjYY2vFrhUyA13ZghsoFc0PBjj1bKSg6Kp3KIqB8tXb6B5+7uPuxlTzy9894+30bOdsOcuOss31YafOFzNxhu8tKx3ad6dQ+OGZIiWPr0shOVZjRhIsG4Fr9e/7z9FTyC0qprHIzZFBw5UvIHIHbnZnSfBJEjhbm8+HnGUSER0gHpjCtmpUOBzBieG9Wf7eVcSP7G13ScUIiwEvLSti83cUloyYbXUqLprVm7YbvWJgxn5WfLaGsvIzpl15NVGSU0aUJ0SxxsZFMuGiA0WX8TEgE+MZsG263mzRZgdAQR44eZumH6aRnzCd7q5P4uASmX3oNs6b8Rtq/hfCjkAjw2skiKRIWAaO15sf1/2Phsvl88PlSyivKOX3wWTzz0MtMHDOduFjjF7sXItSFRIDbnJm0a9OBrp26G11KyDucf4glHywkPWM+OduzSIhLZOakOVw1ZQ6D+w8xujwhWpTQCHBXJqkDTg+q8ZmhRGvN95lfk54xnw8+X0ZFZQVnpg3n7w/P57LR04iNad4sPiHEqTF9gJeVl5G9dROjzxtvdCkh59CRPN5d+SbpGa+ydedmEuNbMXvqDVw1ZQ4D+6YaXZ4QLZ7pA9y52U51dbWMNfYRt9vNt+u+In3ZfFZ9uZzKqkrOGvJrbp9zP5eOnkZMdKzRJQohPEwf4HIRY984ePgA77z/BunLF7B9Vw6tE9twzfSbmDV5jozjFiJIhUCAZ9K6VVu6d+lldCmm43a7+eanL0nPmM/Hq1dQWVXJ8DPO5+4bHmT8qKnERJ/8SiVCCOOFQIBbSU2WDsymOHAwl3dWvsGijAXs2LOV1q3acv2Vt3LV5Ovpd1pgrlsphGg+Uwd4eUU5rhwHN866w+hSgp7b7WbND5+Rvmw+n6xZSVV1FeecOYJ7b36EcRdOJjoq2ugShRBNZOoAz966icqqSpnA8wty8/by9orXeeu919i1dzttW7dn7lW3M3PS9fRNSja6PCFEM5g6wGUJ2YZVV1fz1fefkp4xn0+//oDq6mrOPetCHrjtCcaNnCRrkwgRIkwf4InxrejVrbfRpQSFfQf2sHjFf3lr+Wvsyd1J+7YduWn2XcycPIfTegTPhViFEL5h6gB3uKyktPAZmNXV1Xz53cekL5vPZ998iNvt5oKzR/PnO59izIjLiIyINLpEIYSfmDbAK6sqcebYuf7KW40uxRB7cnex+L3XeGvFf9m3fzcd2nXilmvvYeak60nq3sfo8oQQAWDaAM/euonyinJSk4caXUrAVFVV8cW3H7Fw2Xy+/PYjtNaMGH4xj/3+WS6+4FK5cLAQLYxpA9zu9MzAbAEdmLv37eCt915j8YrXyT2wh07tu3Dbdfcyc9L19Ox2mtHlCSEMYt4Ad1mJj0sI2c65yqpKPv/mQ9KXvcqX330MwIW/Hsdf7n2Oi84bL0fbQgjzBrjNlUlK8lDCwkLmuswA7Nq7nUXLF/D2itfZf3AfnTt24865f2TGxOtkuQAhxHFMGeBVVVVsyrYxe+oNRpfiE5VVlXy6ZiXpGa/y1fefopRi1LnjmDflRUb9ehzh4ab8ZxJC+FmjyaCUigbWAFGe9y/RWj+slHobqJ3K1xrI11oP9VOdx8nZ7qKsvJS0geaegblj91YWvVdztJ13aD9dOnXn7hse4sqJ19Gtcw+jyxNCBDlvDu3KgVFa6yKlVATwjVJqldb6yto3KKWeBY76q8gT2Vw1MzDNuIRsRWUFH3+1gkUZC1jzw2eEhYUx+rzxzJo6lwvPGYvFYjG6RCGESTQa4FprDRR5HkZ4/nTt66pmFs0VwCh/FNgQh2s9sTFx9OllnrU8tu3KYVHGq7z9/hscOpJHt849ueemh5kx8Tq6dOxmdHlCCBPyqnFVKWUB1gF9gRe11j/Ue/l8YL/WevNJPnsjcCNAz549m1eth82ZyeD+Q4L+aLW8opyPVr9Hesar/O+nL7FYLIy54FJmTZnLBWePDvr6hRDBzasA11pXA0OVUq2BDKVUitba4Xl5JvDWL3z2FeAVgGHDhumTvc9b1dXVbMzewIyJ1zX3q/xmy45sFi1fwDvvv8Hh/IP06JrEfbc8xpUTr6VT+y5GlyeECBFNGt6gtc5XSq0GxgEOpVQ4MBU40w+1NWjrzmxKSotJDbIOzLLyMj76cjkLM17lu3VfEW4JZ8yIy5g9dS7n/+qikBvuKIQwnjejUDoAlZ7wjgFGA095Xh4NuLTWu/1Y43GC7RqYOdtdpGcs4N2Vb3Lk6CF6devNA7c9wRWXXkPH9p2NLk8IEcK8OQLvArzuaQcPA97RWq/0vDaDX2g+8Qeb00p0VDT9koy79FdpWSkffrGM9IxX+cH6DRHhEYwdOZFZU+Zy3lkXytG2ECIgvBmFYgMaPNzVWl/n64Ia43BZGdgvzZDJLVlbNpK+fAFLP1hIfsERknr05aHb/8r0S6+mfduOAa9HCNGymWqKn9vtxp5lZdr4WQH7zdKyUlZ+toT0jFf5acO3RIRHMH7UFGZNncs5Z1wgR9tCCMOYKsC37cqhqLiQ1GT/t387c+wsyljA0g/TOVqYT++e/fjTnU8xfcJs2rXp4PffF0KIxpgqwB21HZh+GoFSWlbCik+XkJ4xn3W274mMiGTCRVOZNWUuw884v0Vf+UcIEXxMFeA2l5XIiEiS+wz26fduzN5AesarLPtwEYXFBfRNSubhu/7G5RNm07Z1O5/+lhBC+IqpAtzusjKwX6pP1sIuLilixSfvkp4xH+vGn4iKjGLCRdOYPXUuvxp6rhxtCyGCnmkCXGuNw2XlsounN+t7HC4rCzNeJeOjtygqLqR/74E8+vtnmTZ+Fm1atfVRtUII4X+mCfCde7ZxtDD/lCbwFBUX8t4n75CeMZ8Nm9YRHRXNZRdPZ9aUuQxLGy5H20IIUzJNgNcuIZvWhGtg2pyZLFw2n+UfL6a4pIgBfQbzxB+eY8olM2md2MZfpQohRECYJsAdLisR4RGNdmAWFhWQ8dFi0jPm48haT3RUDJPGXMGsqXM5I+VXcrQthAgZpglwm9NKcp/BREVG/ew1rTXrN64lPWM+yz9+m9KyEgb2S+Uv9/2DqZfMJDG+lQEVCyGEf5kiwLXW2F1Wxo2ceNzzBUVHWbbqLdIz5rMp20ZMdCyTx17JrClzGTp4mBxtCyFCmikCfE/uTo4cPUTqwDPQWpPp+JH0ZfNZ8em7lJaVkJI8lHkPvMjksVeSEJ9odLlCCBEQpghwm7OmA3Nj1gYunnkmzhwHcbHxTL3kKmZPndukjk0hhAgVpgjwTZttAKRnzGfIoDN5+sF/M2nMFcTHJRhcmRBCGMcUAT76vAmUl5cxccwVQXMhByGEMJopAnzo4GEMHTzM6DKEECKoyGLWQghhUhLgQghhUhLgQghhUhLgQghhUhLgQghhUhLgQghhUhLgQghhUhLgQghhUkprHbgfUyoP2BGwH4T2wMEA/t6pkBqbL9jrA6nRV1pqjb201h1OfDKgAR5oSqm1WuugnsIpNTZfsNcHUqOvSI3HkyYUIYQwKQlwIYQwqVAP8FeMLsALUmPzBXt9IDX6itRYT0i3gQshRCgL9SNwIYQIWRLgQghhUqYOcKXUEKXUd0opu1LqfaVUouf5i5VS6zzPr1NKjTrJ5x9RSu1RSq33/I0PwhrbKqU+VUpt9ty2CWCN7ZRSXyqlipRSL/zC543cjt7WaNh29Lz2gFIqRymVpZQae5LPG7Ydm1BjILbjUKXU955tsFYp9SvP85FKqdc8tW9QSo08yecDsR2bW6NvtqPW2rR/wE/ACM/9OcDjnvunA10991OAPSf5/CPAPUFe49PA/Z779wNPBbDGOOA84CbghV/4vJHb0dsajdyOg4ANQBRwGrAFsATZdvS2xkBsx0+ASzz3xwOrPfdvBV7z3O8IrAPCDNqOza3RJ9vR1EfgQDKwxnP/U2AagNbaqrXe63l+IxCtlIoyoD5ofo2TgNc9918HJgewxmKt9TdAmR9+s6maW6Nh29Hz24u11uVa621ADvArP/y+N5pbYyC2owZqzwxaAbX/PxkEfA6gtT4A5ANGTeppbo0+2Y5mD3AHMNFzfzrQo4H3TAOsWuvyk3zHbUopm1JqgT9OB31QYyet9T4Az21Hg2psTDBsx19i5HbsBuyq977dnucaYtR29LbGQGzHO4G/KaV2Ac8AD3ie3wBMUkqFK6VOA87k5P8d+Hs7NrdGn2zHoA9wpdRnSilHA3+TqDkFvFUptQ5IACpO+Oxg4Cngtyf5+n8DfYChwD7g2SCs0SeaU6MXDN+OgXKKNaoGvqqh8btGbkdva/SJRmq8GbhLa90DuAt41fOxBdTsWNYCzwHfAlUNfH0gtmNza/QNf7YTBfIP6A/8WO9xdyAbONfLzycBjmCrEcgCunjudwGyAlmj57nr+IX2ZaO3ozc1GrkdqTk6e6Deax8D5wTTdvS2xkBsR+Aox+aoKKDgJO/7FhhkxHZsbo2+2o5BfwT+S5RSHT23YcBDwEuex62BD6j5D/J/v/D5LvUeTqHm9DKoagRWANd67l8LvBeoGpvwecO2YxMYuR1XADOUUlGe0+p+wI8NfN7I7ehVjQRgO1LTnjzCc38UsNlTc6xSKs5z/2KgSmu96cQPB2I7NrdGfLUd/bmH9/cfcAc1R7DZwDyO7REfAoqB9fX+Onpemw8M89x/E7ADNs8G7RKENbajplNks+e2baBq9Ly2HTgMFFFzajgomLZjE2o0ejs+SM3Ijiw8oxeCcDt6U2MgtuN51Ize2AD8AJzpeT7JU5sT+IyaJVaN2o7NrdEn21Gm0gshhEmZuglFCCFaMglwIYQwKQlwIYQwKQlwIYQwKQlwIYQwKQlwIYQwKQlwIYQwqf8HKY/9o5xVRqYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr458,wedgePops752677.45, 153338.9, 148414.1, 225193.3, 225731.2, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0011 \n",
      "   targetWP, latest drx4 are tWP,dr, 225961.8,-1.6752, 225961.8,0.3948, 225961.8,0.0134, 225961.8,0.0073\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.67523 211397.5473 153338.8731 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.21344 105268.4382 148414.0668 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.30066 220976.9172 225193.2678 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.5048 219141.0861 225731.2403 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 458, giving up w pop 752677.4479414581\n",
      "I am working on tract number 460 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 467 10.0 0 83.5 2.7624 234114.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 473 3 262271.7237016303 1.4722\n",
      "8 yoyos for tract,wedge,wedgePop,r= 473 3 36150.431720621506 0.7361\n",
      "9 yoyos for tract,wedge,wedgePop,r= 473 3 263458.38660486817 1.4792\n",
      "10 yoyos for tract,wedge,wedgePop,r= 473 3 75652.76391222028 1.1077\n",
      "10 yoyos for tract,wedge,wedgePop,r= 473 3 183088.75887118888 1.2934\n",
      "11 yoyos for tract,wedge,wedgePop,r= 473 3 247629.5997383036 1.3863\n",
      "11 yoyos for tract,wedge,wedgePop,r= 473 3 228547.5638001265 1.3399\n",
      "11 yoyos for tract,wedge,wedgePop,r= 473 3 209239.38488085792 1.3167\n",
      "11 yoyos for tract,wedge,wedgePop,r= 473 3 195332.63457363803 1.305\n",
      "I am working on tract number 480 of 3265 tracts\n",
      "I am working on tract number 500 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 6.0 1 37.1 0.7996 260538.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 7.0 1 37.1 0.7727 260538.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 8.0 1 37.1 0.7467 260538.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 9.0 1 37.1 0.7215 260538.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 10.0 1 37.1 0.6972 260538.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 11.0 1 37.1 0.6737 260538.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 12.0 1 37.1 0.651 260538.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 516 13.0 1 37.1 0.6291 260538.8\n",
      "I am working on tract number 520 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 6.0 1 37.5 0.8025 260545.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 7.0 1 37.5 0.7697 260545.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 8.0 1 37.5 0.7382 260545.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 9.0 1 37.5 0.7081 260545.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 10.0 1 37.5 0.6792 260545.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 11.0 1 37.5 0.6514 260545.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 12.0 1 37.5 0.6248 260545.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 528 13.0 1 37.5 0.5993 260545.7\n",
      "I am working on tract number 540 of 3265 tracts\n",
      "I am working on tract number 560 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 564\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 570 5.0 2 90.0 2.2445 201625.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 570 10.0 2 90.0 2.2118 201625.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 570 15.0 2 90.0 2.2042 201625.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 570 2 200887.82526268286 2.1355\n",
      "8 yoyos for tract,wedge,wedgePop,r= 570 2 97512.00621284683 1.0678\n",
      "9 yoyos for tract,wedge,wedgePop,r= 570 2 201625.08305348473 2.1993\n",
      "I am working on tract number 580 of 3265 tracts\n",
      "I am working on tract number 600 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 614 0 236630.4129097591 1.4943\n",
      "8 yoyos for tract,wedge,wedgePop,r= 614 0 67661.2965264456 0.7472\n",
      "9 yoyos for tract,wedge,wedgePop,r= 614 0 236570.254191884 1.4916\n",
      "10 yoyos for tract,wedge,wedgePop,r= 614 0 104691.77948377118 1.1194\n",
      "11 yoyos for tract,wedge,wedgePop,r= 614 0 218503.4917153087 1.3055\n",
      "12 yoyos for tract,wedge,wedgePop,r= 614 0 152103.30645257427 1.2124\n",
      "I am working on tract number 620 of 3265 tracts\n",
      "I am working on tract number 640 of 3265 tracts\n",
      "I am working on tract number 660 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 664\n",
      "we have 2 non-opposing shorted wedges for tract no 672\n",
      "I am working on tract number 680 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 689\n",
      "we have 2 non-opposing shorted wedges for tract no 690\n",
      "I am working on tract number 700 of 3265 tracts\n",
      "I am working on tract number 720 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 724 7.0 0 84.1 1.108 227799.2\n",
      "we have 2 non-opposing shorted wedges for tract no 729\n",
      "I am working on tract number 740 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 742\n",
      "we have 2 non-opposing shorted wedges for tract no 743\n",
      "I am working on tract number 760 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 764 6.0 3 90.0 1.1378 209730.4\n",
      "I am working on tract number 780 of 3265 tracts\n",
      "I am working on tract number 800 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 812 5.0 0 84.5 0.7796 300793.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 2.0 2 90.0 0.8957 210304.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 3.0 2 90.0 0.8238 210304.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 4.0 2 90.0 0.7578 210304.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 5.0 2 90.0 0.697 210304.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 6.0 2 90.0 0.6411 210304.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 7.0 2 90.0 0.5897 210304.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 815 2.0 3 90.0 0.6368 266092.1\n",
      "I am working on tract number 820 of 3265 tracts\n",
      "I am working on tract number 840 of 3265 tracts\n",
      "I am working on tract number 860 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 868\n",
      "we have 2 non-opposing shorted wedges for tract no 873\n",
      "I am working on tract number 880 of 3265 tracts\n",
      "I am working on tract number 900 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 3.0 0 90.0 0.971 204275.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 4.0 0 90.0 0.9133 204275.7\n",
      "I am working on tract number 920 of 3265 tracts\n",
      "I am working on tract number 940 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 946 3 275078.9745208281 1.0441\n",
      "8 yoyos for tract,wedge,wedgePop,r= 946 3 74068.07924253991 0.5221\n",
      "9 yoyos for tract,wedge,wedgePop,r= 946 3 275549.8723654867 1.0479\n",
      "10 yoyos for tract,wedge,wedgePop,r= 946 3 165073.9298912477 0.785\n",
      "11 yoyos for tract,wedge,wedgePop,r= 946 3 255512.25709124166 0.9164\n",
      "11 yoyos for tract,wedge,wedgePop,r= 946 3 230257.64423164286 0.8507\n",
      "11 yoyos for tract,wedge,wedgePop,r= 946 3 198372.36571149903 0.8178\n",
      "12 yoyos for tract,wedge,wedgePop,r= 946 3 182185.82833355243 0.8014\n",
      "I am working on tract number 960 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 961\n",
      "I am working on tract number 980 of 3265 tracts\n",
      "I am working on tract number 1000 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1009 6.0 1 37.7 0.635 273457.3\n",
      "I am working on tract number 1020 of 3265 tracts\n",
      "I am working on tract number 1040 of 3265 tracts\n",
      "I am working on tract number 1060 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1074 0 378759.723907897 1.5041\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1074 0 43900.51893114403 0.752\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1074 0 379380.88356900465 1.507\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1074 0 241605.08725247957 1.1295\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1074 0 67132.01326868072 0.9408\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1074 0 124432.93268213718 1.0352\n",
      "I am working on tract number 1080 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1099 4.0 3 47.9 0.2614 196303.0\n",
      "I am working on tract number 1100 of 3265 tracts\n",
      "I am working on tract number 1120 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1124\n",
      "we have 2 non-opposing shorted wedges for tract no 1125\n",
      "I am working on tract number 1140 of 3265 tracts\n",
      "I am working on tract number 1160 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1160\n",
      "we have 2 non-opposing shorted wedges for tract no 1161\n",
      "we have 2 non-opposing shorted wedges for tract no 1169\n",
      "we have 2 non-opposing shorted wedges for tract no 1170\n",
      "I am working on tract number 1180 of 3265 tracts\n",
      "I am working on tract number 1200 of 3265 tracts\n",
      "I am working on tract number 1220 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1220 7.0 0 104.2 1.2864 271111.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1222 1 353996.23888530815 2.4772\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1222 1 198449.32382735074 1.2386\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1222 1 357860.87053745036 2.5066\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1222 1 251196.4369803832 1.8726\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1222 1 282225.61847596965 2.1896\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1222 1 295569.4304578633 2.3481\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1222 1 329202.64080937643 2.4274\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1222 1 352387.46167286433 2.467\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1222 1 344999.6058682298 2.4472\n",
      "I am working on tract number 1240 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 12.0 0 104.1 2.2034 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 17.0 0 104.1 2.7383 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 18.0 0 104.1 2.6761 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 19.0 0 104.1 2.6153 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 20.0 0 104.1 2.5559 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 21.0 0 104.1 2.4979 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 22.0 0 104.1 2.4411 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 23.0 0 104.1 2.3857 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 24.0 0 104.1 2.3315 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 25.0 0 104.1 2.2785 194258.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 26.0 0 104.1 2.2268 194258.1\n",
      "loop31.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.113, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 31.0 0 104.1 3.2882 194258.1\n",
      "loop32.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0747, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 32.0 0 104.1 3.2135 194258.1\n",
      "loop33.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.073, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 33.0 0 104.1 3.1405 194258.1\n",
      "loop34.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0713, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 34.0 0 104.1 3.0692 194258.1\n",
      "loop35.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0697, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 35.0 0 104.1 2.9995 194258.1\n",
      "loop36.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0681, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 36.0 0 104.1 2.9313 194258.1\n",
      "loop37.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0666, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 37.0 0 104.1 2.8648 194258.1\n",
      "loop38.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0651, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 38.0 0 104.1 2.7997 194258.1\n",
      "loop39.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0636, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.73609 194258.0722 194258.0722 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.87855 189282.6056 188527.9484 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.97366 193711.6306 188536.5153 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.70024 187436.0143 188071.892 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1242 39.0 0 104.1 2.7361 194258.1\n",
      "loop40.0, tr1242,wedgePops759394.43, 194258.1, 188527.9, 188536.5, 188071.9, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5110 \n",
      "   targetWP, latest drx4 are tWP,dr, 188419.1,-0.0622, 188419.1,-0.0049, 188419.1,-0.0225, 188419.1,0.0104\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.67393 194258.0722 194258.0722 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.87855 189282.6056 188527.9484 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.97366 193711.6306 188536.5153 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.70024 187436.0143 188071.892 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1242, giving up w pop 759394.4278453066\n",
      "we have 2 non-opposing shorted wedges for tract no 1252\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1258 3.0 3 36.4 0.709 273472.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1258 4.0 3 36.4 0.6627 273472.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1258 5.0 3 36.4 0.6195 273472.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1258 6.0 3 36.4 0.579 273472.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1258 7.0 3 36.4 0.5412 273472.8\n",
      "I am working on tract number 1260 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1266 1 367404.3780754842 0.9615\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1266 1 41232.13338894356 0.4808\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1266 1 367775.21932729956 0.9653\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1266 1 138513.27566823972 0.723\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1266 1 336860.50658477296 0.8442\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1266 1 251317.42818371046 0.7836\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1266 1 189635.88805812816 0.7533\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1266 1 221029.38783442002 0.7684\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1267 3.0 3 36.2 0.7808 392625.9\n",
      "we have 2 non-opposing shorted wedges for tract no 1276\n",
      "we have 2 non-opposing shorted wedges for tract no 1277\n",
      "I am working on tract number 1280 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 3.0 0 109.7 0.8275 190627.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 4.0 0 109.7 0.8203 190627.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 5.0 0 109.7 0.8131 190627.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 6.0 0 109.7 0.806 190627.5\n",
      "I am working on tract number 1300 of 3265 tracts\n",
      "I am working on tract number 1320 of 3265 tracts\n",
      "I am working on tract number 1340 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1356 3.0 0 90.0 0.9934 193999.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1356 4.0 0 90.0 0.9719 193999.5\n",
      "I am working on tract number 1360 of 3265 tracts\n",
      "I am working on tract number 1380 of 3265 tracts\n",
      "I am working on tract number 1400 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1414 2 284608.69658259774 1.1403\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1414 2 54687.37090015598 0.5702\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1414 2 292602.5517295053 1.1892\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1414 2 151104.26896297713 0.8797\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1414 2 266100.9731125891 1.0345\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1414 2 227281.53875833884 0.9571\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1414 2 183349.70348089817 0.9184\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1414 2 205174.9773194037 0.9377\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1414 2 193502.26283009216 0.9281\n",
      "I am working on tract number 1420 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1434 0 195006.15413120852 0.8228\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1434 0 61220.99801526163 0.4114\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1434 0 286631.70628991874 0.9429\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1434 0 78433.81817452551 0.6771\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1434 0 181157.5575181522 0.81\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1434 0 238713.8564650794 0.8764\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1434 0 214458.81624590128 0.8432\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1434 0 198966.1791418946 0.8266\n",
      "I am working on tract number 1440 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1449 2 217555.60423304274 0.9291\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1449 2 72030.90078966654 0.4645\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1449 2 258412.52381243647 0.9858\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1449 2 96451.92842226234 0.7252\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1449 2 135484.16900637755 0.8555\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1449 2 209042.92364634873 0.9207\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1449 2 169384.84869675446 0.8881\n",
      "I am working on tract number 1460 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1460 2 346811.66806912253 1.2096\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1460 2 32560.835607905756 0.6048\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1460 2 338112.9957607848 1.1503\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1460 2 174349.93545621535 0.8776\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1460 2 312622.30143966293 1.0139\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1460 2 283755.05018891493 0.9458\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1460 2 241639.191722896 0.9117\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1460 2 199450.2304566862 0.8946\n",
      "I am working on tract number 1480 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1488 3 362378.5615002202 1.76\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1488 3 73290.61856441345 0.88\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1488 3 354804.2189454064 1.6803\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1488 3 277662.7574564132 1.2802\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1488 3 95687.01992010206 1.0801\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1488 3 110747.56261699904 1.1801\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1488 3 152685.74204339297 1.2301\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1488 3 220075.442894955 1.2551\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1488 3 177669.86606299615 1.2426\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1488 3 195996.58738503177 1.2489\n",
      "I am working on tract number 1500 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1506\n",
      "we have 2 non-opposing shorted wedges for tract no 1507\n",
      "I am working on tract number 1520 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1520\n",
      "we have 2 non-opposing shorted wedges for tract no 1521\n",
      "we have 2 non-opposing shorted wedges for tract no 1531\n",
      "I am working on tract number 1540 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1551\n",
      "I am working on tract number 1560 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1568 2.0 1 90.0 0.2022 213482.3\n",
      "I am working on tract number 1580 of 3265 tracts\n",
      "I am working on tract number 1600 of 3265 tracts\n",
      "I am working on tract number 1620 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1622\n",
      "I am working on tract number 1640 of 3265 tracts\n",
      "I am working on tract number 1660 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1663\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1664 1 617625.0095732847 2.5127\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1664 1 25058.549179014633 1.2564\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1664 1 620132.7962039317 2.5488\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1664 1 212912.9105103208 1.9026\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1664 1 559431.8655623412 2.2257\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1664 1 398325.0789908925 2.0642\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1664 1 307615.6051650289 1.9834\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1664 1 349567.84307479084 2.0238\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1664 1 371084.6429889754 2.044\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1678 5.0 1 41.8 0.6108 263311.0\n",
      "I am working on tract number 1680 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1695\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1699 3 340943.70455623325 2.1447\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1699 3 128027.64093681687 1.0724\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1699 3 340851.1315806707 2.144\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1699 3 171559.11866445595 1.6082\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1699 3 318034.3192304787 1.8761\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1699 3 301016.5546079247 1.7421\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1699 3 258859.99615959963 1.6751\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1699 3 192429.39073335298 1.6417\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1699 3 213793.14513695106 1.6584\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1699 3 234697.9730506142 1.6668\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1699 3 222759.99829932832 1.6626\n",
      "I am working on tract number 1700 of 3265 tracts\n",
      "I am working on tract number 1720 of 3265 tracts\n",
      "I am working on tract number 1740 of 3265 tracts\n",
      "I am working on tract number 1760 of 3265 tracts\n",
      "I am working on tract number 1780 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1780\n",
      "we have 2 non-opposing shorted wedges for tract no 1783\n",
      "I am working on tract number 1800 of 3265 tracts\n",
      "I am working on tract number 1820 of 3265 tracts\n",
      "I am working on tract number 1840 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1845\n",
      "we have 2 non-opposing shorted wedges for tract no 1851\n",
      "I am working on tract number 1860 of 3265 tracts\n",
      "I am working on tract number 1880 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1898 3 399200.1999158509 2.2698\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1898 3 77455.9090355736 1.1349\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1898 3 388568.9331184907 2.1865\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1898 3 321085.75920216425 1.6607\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1898 3 109514.93055780686 1.3978\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1898 3 127463.7651421099 1.5293\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1898 3 229608.30375698023 1.595\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1898 3 151118.41141404974 1.5621\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1898 3 179988.9492971491 1.5786\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1898 3 200019.87392121597 1.5868\n",
      "I am working on tract number 1900 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1905\n",
      "I am working on tract number 1920 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1935 3 331022.6060593382 2.0264\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1935 3 50098.40865122154 1.0132\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1935 3 340870.9713359798 2.0953\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1935 3 259012.08262840813 1.5542\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1935 3 71128.32280073632 1.2837\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1935 3 89358.76502401239 1.419\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1935 3 117342.51265379298 1.4866\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1935 3 183860.09425639932 1.5204\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1935 3 235203.82554949514 1.5373\n",
      "I am working on tract number 1940 of 3265 tracts\n",
      "I am working on tract number 1960 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1969 7.0 0 126.0 0.6274 214397.3\n",
      "I am working on tract number 1980 of 3265 tracts\n",
      "I am working on tract number 2000 of 3265 tracts\n",
      "I am working on tract number 2020 of 3265 tracts\n",
      "I am working on tract number 2040 of 3265 tracts\n",
      "I am working on tract number 2060 of 3265 tracts\n",
      "I am working on tract number 2080 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2083 9.0 0 120.5 0.7687 327413.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2086 3.0 0 98.9 0.6898 226740.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2087 3 335805.43037169584 1.5269\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2087 3 31127.155346692773 0.7634\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2087 3 335742.6698505861 1.5266\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2087 3 122040.1941146852 1.145\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2087 3 306703.48887961893 1.3358\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2087 3 283980.72874900413 1.2404\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2087 3 237672.09877762003 1.1927\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2087 3 172244.46982088994 1.1689\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2087 3 214323.96757231932 1.1808\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2087 3 193529.36749331234 1.1748\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2087 3 182798.33440561005 1.1719\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2089 2.0 3 90.0 0.8909 211490.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2089 3.0 3 90.0 0.8159 211490.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2089 4.0 3 90.0 0.7472 211490.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2089 5.0 3 90.0 0.6843 211490.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2089 6.0 3 90.0 0.6267 211490.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2089 7.0 3 90.0 0.5739 211490.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2090 7.0 0 100.4 0.6961 228420.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2096 1 302254.2344263374 1.2208\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2096 1 89042.27855861021 0.6104\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2096 1 302764.7056845091 1.2229\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2096 1 217241.23242979916 0.9166\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2096 1 102276.93958653531 0.7635\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2096 1 141321.53347189035 0.8401\n",
      "I am working on tract number 2100 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2102\n",
      "we have 2 non-opposing shorted wedges for tract no 2114\n",
      "we have 2 non-opposing shorted wedges for tract no 2115\n",
      "I am working on tract number 2120 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2136 1 366767.36970873113 1.0525\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2136 1 38080.839501322305 0.5262\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2136 1 365574.30892566446 1.0449\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2136 1 144633.378894395 0.7855\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2136 1 340968.08711753646 0.9152\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2136 1 266246.2459502226 0.8504\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2136 1 203355.47676568315 0.818\n",
      "I am working on tract number 2140 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2141 1 252169.78038506585 0.6082\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2141 1 7743.742460621637 0.3041\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2141 1 259561.31372076232 0.6362\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2141 1 78224.12644972987 0.4701\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2141 1 211931.63560006942 0.5531\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2152 1 391167.3147503234 1.0985\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2152 1 59716.344638201874 0.5493\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2152 1 391169.2464356519 1.0985\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2152 1 137900.31925063537 0.8239\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2152 1 354283.51279743866 0.9612\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2152 1 249238.47814134532 0.8926\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2152 1 182910.29394512944 0.8582\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2152 1 213360.73560974307 0.8754\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2152 1 230900.9562345956 0.884\n",
      "I am working on tract number 2160 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2161 7.0 1 41.6 0.8113 269213.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2161 8.0 1 41.6 0.7376 269213.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2161 9.0 1 41.6 0.6707 269213.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2161 10.0 1 41.6 0.6098 269213.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2176 2.0 2 90.0 0.1095 223266.4\n",
      "I am working on tract number 2180 of 3265 tracts\n",
      "I am working on tract number 2200 of 3265 tracts\n",
      "I am working on tract number 2220 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2222 4.0 1 90.0 0.5918 216996.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2232 1 343726.20004199 1.9867\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2232 1 74931.04792967794 0.9934\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2232 1 454177.2484942946 2.0204\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2232 1 101096.44544722728 1.5069\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2232 1 206317.51540747535 1.7636\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2232 1 240184.06728014423 1.892\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2232 1 284956.7106703457 1.9562\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2232 1 348430.01469342975 1.9883\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2232 1 306172.7526152307 1.9722\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2232 1 323232.9358399369 1.9802\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2237 6.0 1 37.6 0.6302 276190.7\n",
      "I am working on tract number 2240 of 3265 tracts\n",
      "I am working on tract number 2260 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2263\n",
      "I am working on tract number 2280 of 3265 tracts\n",
      "I am working on tract number 2300 of 3265 tracts\n",
      "I am working on tract number 2320 of 3265 tracts\n",
      "I am working on tract number 2340 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2344 0 219910.19999714932 2.5079\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2344 0 99269.01096862345 1.2539\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2344 0 219841.92461262614 2.5065\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2344 0 136791.02010070428 1.8802\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2344 0 156794.3192692437 2.1934\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2344 0 201500.36384414538 2.3499\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2344 0 165391.34941793844 2.2717\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2344 0 180263.9047205487 2.3108\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2349 9.0 0 86.9 2.6007 202793.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2349 14.0 0 86.9 2.5788 202793.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2349 19.0 0 86.9 2.5842 202793.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2349 0 202793.84661511373 2.686\n",
      "I am working on tract number 2360 of 3265 tracts\n",
      "I am working on tract number 2380 of 3265 tracts\n",
      "I am working on tract number 2400 of 3265 tracts\n",
      "I am working on tract number 2420 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2425 2 381864.3553119874 1.3814\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2425 2 69379.10069770936 0.6907\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2425 2 381679.39465992386 1.3802\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2425 2 248608.03301697865 1.0355\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2425 2 92814.54530545496 0.8631\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2425 2 129890.03577373823 0.9493\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2425 2 184363.32036887485 0.9924\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2425 2 217974.51927604846 1.0139\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2425 2 200640.37322702058 1.0031\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2426 1 331649.8337048538 1.2822\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2426 1 31543.99824531778 0.6411\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2426 1 319687.7837208037 1.2038\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2426 1 212469.7733578611 0.9225\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2426 1 55796.1511179035 0.7818\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2426 1 103698.99186054533 0.8521\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2426 1 148977.70024646504 0.8873\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2426 1 177845.96584868565 0.9049\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2426 1 194773.5591471776 0.9137\n",
      "we have 2 non-opposing shorted wedges for tract no 2427\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2428 6.0 0 127.6 2.4218 262234.0\n",
      "I am working on tract number 2440 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2451\n",
      "I am working on tract number 2460 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2462 5.0 0 134.5 0.7648 335123.2\n",
      "I am working on tract number 2480 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2495 3.0 0 84.6 0.1249 216075.1\n",
      "I am working on tract number 2500 of 3265 tracts\n",
      "I am working on tract number 2520 of 3265 tracts\n",
      "I am working on tract number 2540 of 3265 tracts\n",
      "I am working on tract number 2560 of 3265 tracts\n",
      "I am working on tract number 2580 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2584 3 362603.3077035633 2.1087\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2584 3 62080.96875597787 1.0544\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2584 3 362625.6356595519 2.1089\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2584 3 269579.902787085 1.5816\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2584 3 83911.09734526198 1.318\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2584 3 101960.81190356088 1.4498\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2584 3 126735.5444786193 1.5157\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2584 3 179977.435386088 1.5487\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2584 3 237677.3281219766 1.5652\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2584 3 206268.13444312417 1.5569\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2584 3 222786.08723806724 1.561\n",
      "I am working on tract number 2600 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2612 4.0 0 90.0 0.5922 196461.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2612 5.0 0 90.0 0.5738 196461.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2612 6.0 0 90.0 0.556 196461.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2612 7.0 0 90.0 0.5388 196461.4\n",
      "I am working on tract number 2620 of 3265 tracts\n",
      "I am working on tract number 2640 of 3265 tracts\n",
      "I am working on tract number 2660 of 3265 tracts\n",
      "I am working on tract number 2680 of 3265 tracts\n",
      "I am working on tract number 2700 of 3265 tracts\n",
      "I am working on tract number 2720 of 3265 tracts\n",
      "I am working on tract number 2740 of 3265 tracts\n",
      "I am working on tract number 2760 of 3265 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2767 3.0 1 90.0 0.3186 204508.1\n",
      "I am working on tract number 2780 of 3265 tracts\n",
      "I am working on tract number 2800 of 3265 tracts\n",
      "I am working on tract number 2820 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2820\n",
      "we have 2 non-opposing shorted wedges for tract no 2824\n",
      "we have 2 non-opposing shorted wedges for tract no 2832\n",
      "I am working on tract number 2840 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2846 1 495367.1306088497 1.4848\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2846 1 140720.92020913254 0.7424\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2846 1 495724.68761072564 1.4882\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2846 1 252408.6633104236 1.1153\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2846 1 472165.456496699 1.3017\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2846 1 419330.1094241315 1.2085\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2846 1 340491.42686811404 1.1619\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2846 1 384137.70445423253 1.1852\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2846 1 364159.72397278633 1.1735\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2854 2.0 2 90.0 0.6703 196956.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2854 3.0 2 90.0 0.6483 196956.9\n",
      "I am working on tract number 2860 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2860 3 400911.1265103216 2.1288\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2860 3 80584.6817469718 1.0644\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2860 3 399344.92028463836 2.1173\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2860 3 265074.55093345535 1.5909\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2860 3 101994.06598291156 1.3276\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2860 3 122431.9768368436 1.4593\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2860 3 150053.44612176064 1.5251\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2860 3 181349.56447955378 1.558\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2872 10.0 0 128.4 1.0127 304894.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2872 11.0 0 128.4 0.8315 304894.3\n",
      "I am working on tract number 2880 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2890\n",
      "I am working on tract number 2900 of 3265 tracts\n",
      "I am working on tract number 2920 of 3265 tracts\n",
      "I am working on tract number 2940 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2947 3 23875.5500744991 0.7523\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2947 3 253266.9858217328 2.3633\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2947 3 251992.49150366228 1.5578\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2947 3 71988.45715974184 1.1551\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2947 3 211471.83038529952 1.3564\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2947 3 103356.71129387723 1.2558\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2947 3 155754.4005661869 1.3061\n",
      "I am working on tract number 2960 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2964\n",
      "I am working on tract number 2980 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2983 2 195341.16730675477 0.7907\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2983 2 30313.785942688875 0.3953\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2983 2 306814.789760963 0.8606\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2983 2 49311.30005087334 0.628\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2983 2 99060.80517484591 0.7443\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2983 2 217076.0846255087 0.8025\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2983 2 147653.18836681917 0.7734\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2988 3 280986.3945028851 2.5427\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2988 3 60341.103480385354 1.2713\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2988 3 299331.3239714498 2.6189\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2988 3 130795.9205945044 1.9451\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2988 3 158765.2138452805 2.282\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2988 3 200040.44485853688 2.4505\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2988 3 273882.32384798734 2.5347\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2988 3 218324.85855033604 2.4926\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2988 3 252057.11473938456 2.5137\n",
      "I am working on tract number 3000 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3001 3 357502.21808741183 1.9828\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3001 3 81744.94503409415 0.9914\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3001 3 348460.13377560227 1.9444\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3001 3 119965.29075687451 1.4679\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3001 3 281216.2245157263 1.7062\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3001 3 240936.22532485725 1.587\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3001 3 146275.91128120018 1.5275\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3001 3 195977.83509057295 1.5572\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3001 3 164638.44639926386 1.5424\n",
      "14 yoyos for tract,wedge,wedgePop,r= 3001 3 176969.3744618785 1.5498\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3014 3 374535.740248785 2.1876\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3014 3 71068.20518480503 1.0938\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3014 3 374760.367909207 2.1885\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3014 3 287341.5636118547 1.6411\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3014 3 92333.34425916683 1.3675\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3014 3 111483.02607805759 1.5043\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3014 3 140682.23199052163 1.5727\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3014 3 207550.74340880668 1.6069\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3014 3 261515.55859361766 1.624\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3014 3 241323.2115327578 1.6155\n",
      "I am working on tract number 3020 of 3265 tracts\n",
      "I am working on tract number 3040 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3056\n",
      "I am working on tract number 3060 of 3265 tracts\n",
      "I am working on tract number 3080 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3086 1 209269.93250517698 2.313\n",
      "we have 2 non-opposing shorted wedges for tract no 3096\n",
      "I am working on tract number 3100 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3105\n",
      "we have 2 non-opposing shorted wedges for tract no 3106\n",
      "we have 2 non-opposing shorted wedges for tract no 3107\n",
      "we have 2 non-opposing shorted wedges for tract no 3114\n",
      "I am working on tract number 3120 of 3265 tracts\n",
      "I am working on tract number 3140 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3152\n",
      "I am working on tract number 3160 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3173\n",
      "I am working on tract number 3180 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3194\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3195 5.0 0 127.5 1.1227 285728.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3195 6.0 0 127.5 0.9998 285728.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3195 7.0 0 127.5 0.8904 285728.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3195 8.0 0 127.5 0.7929 285728.8\n",
      "I am working on tract number 3200 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3217\n",
      "we have 2 non-opposing shorted wedges for tract no 3219\n",
      "I am working on tract number 3220 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3222\n",
      "we have 2 non-opposing shorted wedges for tract no 3224\n",
      "we have 2 non-opposing shorted wedges for tract no 3229\n",
      "we have 2 non-opposing shorted wedges for tract no 3233\n",
      "I am working on tract number 3240 of 3265 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3245\n",
      "I am working on tract number 3260 of 3265 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3263 3 311093.20269316493 1.5346\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3263 3 33370.66652160237 0.7673\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3263 3 313787.39959734655 1.5683\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3263 3 224139.89448143798 1.1678\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3263 3 56223.03420084363 0.9675\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3263 3 68589.17762898083 1.0677\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3263 3 98921.10536530697 1.1177\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3263 3 149405.72242898808 1.1428\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3263 3 197048.83483166806 1.1553\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3263 3 171703.43090895365 1.149\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.000060843902439\n",
      "Average and max number of wedgePop loops per tract were:  6.6459418070444105 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.9449304437569711 4.273461549776045\n",
      "calculated statewide vote was 0.40692785156957845, should have been 0.4134095626719308\n",
      "calcd statewide Hispanic pop was 0.1567273194377342, should have been 0.1620396043029885\n",
      "calcd statewide Black pop was 0.12398072027423601, should have been 0.13740149601943863\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.35834609494640124\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0001435980852298\n",
      "Average and max number of wedgePop loops per tract were:  6.346843853820598 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.5376799214761454 12.37714006407971 for  MN\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start IL 10:45, end 12:29\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IL 19Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"19Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "458 1.0 ( -89.96634255711946 38.55372296379443 ) 0.5825052621730875 t, pop/target,(x,y), pctR\n",
      "1242 1.01 ( -90.00320404428881 38.66235318826362 ) 0.5810721712111351 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67 0.7708331522802127 1842 pop,GOP 0.6590662323561346\n",
      "71 0.6001606628696271 202 pop,GOP 0.6534653465346535\n",
      "81 0.7874917694264338 663 pop,GOP 0.6168929110105581\n",
      "112 0.7847883692115497 144 pop,GOP 0.6319444444444444\n",
      "120 0.7281689511397273 222 pop,GOP 0.7792792792792793\n",
      "121 0.7553761827972489 2010 pop,GOP 0.6701492537313433\n",
      "122 0.7384694517857902 484 pop,GOP 0.743801652892562\n",
      "212 0.7894528597552886 183 pop,GOP 0.6666666666666666\n",
      "214 0.7215014043383368 83 pop,GOP 0.6385542168674698\n",
      "216 0.6136137538208862 454 pop,GOP 0.6277533039647577\n",
      "217 0.610266540136921 666 pop,GOP 0.5885885885885885\n",
      "218 0.6105636904020771 699 pop,GOP 0.6223175965665236\n",
      "220 0.7394012761268429 824 pop,GOP 0.5012135922330098\n",
      "221 0.764194386373474 1169 pop,GOP 0.5098374679213002\n",
      "224 0.6347037990215098 569 pop,GOP 0.6291739894551845\n",
      "225 0.7416033259386979 347 pop,GOP 0.6282420749279539\n",
      "228 0.6877150810040873 335 pop,GOP 0.5791044776119403\n",
      "229 0.7579315559446376 216 pop,GOP 0.5046296296296297\n",
      "231 0.6935674288396059 735 pop,GOP 0.4557823129251701\n",
      "235 0.6778213308941635 200 pop,GOP 0.655\n",
      "239 0.7082022839072447 412 pop,GOP 0.3422330097087379\n",
      "240 0.7214990129353827 582 pop,GOP 0.4536082474226804\n",
      "293 0.7947355944299267 499 pop,GOP 0.8196392785571143\n",
      "487 0.7259145846226274 641 pop,GOP 0.3338533541341654\n",
      "488 0.6567856334707063 579 pop,GOP 0.3471502590673575\n",
      "489 0.7078758387180679 507 pop,GOP 0.2445759368836292\n",
      "688 0.7898845254685265 217 pop,GOP 0.8248847926267281\n",
      "691 0.7775093015380632 251 pop,GOP 0.8326693227091634\n",
      "692 0.7662255121177013 172 pop,GOP 0.7616279069767442\n",
      "693 0.7274135583955038 75 pop,GOP 0.7866666666666666\n",
      "700 0.7384851559044602 67 pop,GOP 0.7910447761194029\n",
      "1049 0.7842301024649597 643 pop,GOP 0.6749611197511665\n",
      "1051 0.777037286634522 348 pop,GOP 0.6494252873563219\n",
      "1052 0.7514796466825845 796 pop,GOP 0.6268844221105527\n",
      "1055 0.7406320619616065 579 pop,GOP 0.687392055267703\n",
      "1061 0.786501707263676 655 pop,GOP 0.6198473282442748\n",
      "1069 0.7960766025997105 591 pop,GOP 0.2961082910321489\n",
      "1100 0.7538464144164252 887 pop,GOP 0.5456595264937993\n",
      "1102 0.7886841442227551 799 pop,GOP 0.5969962453066333\n",
      "1109 0.7105611507176817 996 pop,GOP 0.5612449799196787\n",
      "1119 0.7675488375550412 796 pop,GOP 0.5603015075376885\n",
      "1147 0.7932251864496844 689 pop,GOP 0.5674891146589259\n",
      "1149 0.7982995993578412 915 pop,GOP 0.6087431693989072\n",
      "1413 0.7992150182515007 306 pop,GOP 0.33986928104575165\n",
      "1422 0.7873559498968329 1393 pop,GOP 0.29504666188083273\n",
      "1426 0.7418061247177801 517 pop,GOP 0.2011605415860735\n",
      "1427 0.7537591766904113 564 pop,GOP 0.20390070921985815\n",
      "1429 0.7959134074611907 1290 pop,GOP 0.22635658914728682\n",
      "1430 0.7878104668831168 925 pop,GOP 0.09081081081081081\n",
      "1434 0.7397769920971181 421 pop,GOP 0.09263657957244656\n",
      "1437 0.7533937664261808 810 pop,GOP 0.22098765432098766\n",
      "1474 0.7467881732896895 548 pop,GOP 0.06934306569343066\n",
      "1475 0.7403096155670856 745 pop,GOP 0.16375838926174496\n",
      "1476 0.7449793084466233 623 pop,GOP 0.18298555377207062\n",
      "1500 0.7055833968770168 2187 pop,GOP 0.6881572930955647\n",
      "1521 0.5235057162749092 632 pop,GOP 0.6107594936708861\n",
      "1522 0.6540943829109198 404 pop,GOP 0.6806930693069307\n",
      "1523 0.775936562937673 562 pop,GOP 0.30604982206405695\n",
      "1529 0.7662993797641554 609 pop,GOP 0.1921182266009852\n",
      "1548 0.09514060811662545 281 pop,GOP 0.797153024911032\n",
      "2224 0.7992149053983294 633 pop,GOP 0.6398104265402843\n",
      "2245 0.7647443467040566 828 pop,GOP 0.6799516908212561\n",
      "2409 0.5452343800661372 508 pop,GOP 0.8503937007874016\n",
      "2425 0.702498074387135 2257 pop,GOP 0.6530793088170137\n",
      "2426 0.05669557995583648 2110 pop,GOP 0.64739336492891\n",
      "2427 0.6773310174584405 367 pop,GOP 0.7193460490463215\n",
      "2428 0.6808465990723662 2175 pop,GOP 0.6441379310344828\n",
      "2429 0.6735430294189376 2451 pop,GOP 0.6682986536107711\n",
      "2443 0.6513446400330091 2198 pop,GOP 0.6574158325750682\n",
      "2446 0.6995412072962841 2272 pop,GOP 0.6668133802816901\n",
      "2447 0.3303334341392815 1988 pop,GOP 0.6498993963782697\n",
      "2449 0.679143591290006 2226 pop,GOP 0.6545372866127583\n",
      "2452 0.6561683518202402 2341 pop,GOP 0.6723622383596753\n",
      "2453 0.6564631133047443 2228 pop,GOP 0.6678635547576302\n",
      "2482 0.7809179212843782 2011 pop,GOP 0.669816011934361\n",
      "2487 0.7315906484292108 538 pop,GOP 0.6654275092936803\n",
      "2603 0.7727004639688233 1004 pop,GOP 0.6543824701195219\n",
      "2632 0.7220609499706191 2696 pop,GOP 0.7084569732937686\n",
      "2636 0.7316872420192798 2574 pop,GOP 0.6837606837606838\n",
      "2638 0.7862750168475133 316 pop,GOP 0.8164556962025317\n",
      "2641 0.7703106590871518 2815 pop,GOP 0.6891651865008881\n",
      "2642 0.7020723881613058 2377 pop,GOP 0.6739587715607909\n",
      "2643 0.6815049089748523 2678 pop,GOP 0.6852128454070202\n",
      "3092 0.7758536456821552 379 pop,GOP 0.20316622691292877\n",
      "3093 0.7985527805882421 505 pop,GOP 0.11287128712871287\n",
      "3097 0.7995751927273264 441 pop,GOP 0.25396825396825395\n",
      "3101 0.7941488318239719 313 pop,GOP 0.19169329073482427\n",
      "3108 0.7973293934247704 305 pop,GOP 0.16065573770491803\n",
      "3123 0.7948145401110783 303 pop,GOP 0.28052805280528054\n",
      "3129 0.7902859217070083 359 pop,GOP 0.21448467966573817\n",
      "3190 0.7837543991745279 366 pop,GOP 0.21584699453551912\n",
      "3191 0.7908371303010739 317 pop,GOP 0.19242902208201892\n",
      "3212 0.7170509526404147 2109 pop,GOP 0.6699857752489331\n",
      "3213 0.7017578340066716 457 pop,GOP 0.7045951859956237\n",
      "3214 0.7078673045857644 2202 pop,GOP 0.670299727520436\n",
      "3215 0.7102578774698542 2178 pop,GOP 0.6662075298438935\n",
      "3216 0.7080172192992737 2054 pop,GOP 0.6592015579357352\n",
      "3218 0.6921676714629509 2334 pop,GOP 0.6928020565552699\n",
      "3230 0.7141455605075305 1920 pop,GOP 0.6708333333333333\n",
      "3232 0.6861648189514747 602 pop,GOP 0.7973421926910299\n",
      "3233 0.7221325665713282 1809 pop,GOP 0.65616362631288\n",
      "3235 0.7085785134918029 1910 pop,GOP 0.6670157068062827\n",
      "3236 0.7865297454656768 1002 pop,GOP 0.2654690618762475\n",
      "4428 0.7141618497677666 589 pop,GOP 0.05602716468590832\n",
      "4436 0.775037621488742 1474 pop,GOP 0.09905020352781546\n",
      "4437 0.7136253974786139 780 pop,GOP 0.308974358974359\n",
      "4440 0.7738381857722454 612 pop,GOP 0.09803921568627451\n",
      "4441 0.7083158563908789 660 pop,GOP 0.22272727272727272\n",
      "4442 0.7897508601520199 647 pop,GOP 0.16228748068006182\n",
      "4444 0.7935584411450319 584 pop,GOP 0.2842465753424658\n",
      "4447 0.7189796114393497 997 pop,GOP 0.23069207622868607\n",
      "4450 0.7078914935596342 802 pop,GOP 0.09725685785536159\n",
      "4580 0.7553332358296049 966 pop,GOP 0.4979296066252588\n",
      "4581 0.7667759264532958 746 pop,GOP 0.39812332439678283\n",
      "4583 0.7673694001982014 977 pop,GOP 0.4165813715455476\n",
      "4584 0.7970787216376024 858 pop,GOP 0.4627039627039627\n",
      "4587 0.7307174839791221 856 pop,GOP 0.4077102803738318\n",
      "4655 0.7468861452976552 535 pop,GOP 0.18130841121495328\n",
      "4656 0.675645952176552 483 pop,GOP 0.30434782608695654\n",
      "4657 0.6673686020198328 668 pop,GOP 0.33083832335329344\n",
      "4658 0.7459710846356131 475 pop,GOP 0.2863157894736842\n",
      "4659 0.6774908739717485 704 pop,GOP 0.3352272727272727\n",
      "4662 0.6257269344559488 629 pop,GOP 0.43879173290937995\n",
      "4663 0.7323844236463105 818 pop,GOP 0.26894865525672373\n",
      "4664 0.7751369089018577 751 pop,GOP 0.3209054593874834\n",
      "4665 0.7657718628200382 653 pop,GOP 0.24655436447166923\n",
      "4676 0.7989766260098301 679 pop,GOP 0.5051546391752577\n",
      "4677 0.7400074326289966 528 pop,GOP 0.3806818181818182\n",
      "4678 0.7289864928661034 451 pop,GOP 0.4878048780487805\n",
      "4698 0.7937637000348903 727 pop,GOP 0.37551581843191195\n",
      "4699 0.7624822880279055 477 pop,GOP 0.2830188679245283\n",
      "4795 0.7815879077411384 493 pop,GOP 0.5598377281947262\n",
      "4798 0.7971534194870432 839 pop,GOP 0.4231227651966627\n",
      "4799 0.7615795185964247 655 pop,GOP 0.42290076335877863\n",
      "4802 0.7954961376616535 713 pop,GOP 0.5035063113604488\n",
      "4805 0.752420167271976 661 pop,GOP 0.5370650529500757\n",
      "4806 0.7837436089503351 675 pop,GOP 0.5822222222222222\n",
      "4849 0.7946701680089303 815 pop,GOP 0.5570552147239264\n",
      "4854 0.7882310675632924 947 pop,GOP 0.6515311510031679\n",
      "4859 0.7894766093732051 934 pop,GOP 0.5182012847965739\n",
      "4862 0.7301093897489569 874 pop,GOP 0.5\n",
      "4863 0.7676719068486043 1033 pop,GOP 0.5130687318489835\n",
      "4866 0.5872385886398556 606 pop,GOP 0.5825082508250825\n",
      "4868 0.6208557706057396 580 pop,GOP 0.6051724137931035\n",
      "4870 0.6819057790795022 837 pop,GOP 0.5675029868578255\n",
      "4871 0.6108193845592691 672 pop,GOP 0.6220238095238095\n",
      "4872 0.5949273357492856 579 pop,GOP 0.6027633851468048\n",
      "4873 0.6986381784867539 703 pop,GOP 0.46372688477951635\n",
      "4874 0.7308683263890879 487 pop,GOP 0.46406570841889117\n",
      "4876 0.6375776301933025 672 pop,GOP 0.5595238095238095\n",
      "4877 0.7817771536622712 799 pop,GOP 0.20901126408010012\n",
      "5605 0.7772329488055276 332 pop,GOP 0.28012048192771083\n",
      "5611 0.7067958103304196 554 pop,GOP 0.2148014440433213\n",
      "5618 0.7797232811361251 553 pop,GOP 0.12839059674502712\n",
      "5619 0.7423294861543018 816 pop,GOP 0.08455882352941177\n",
      "5623 0.7283922816420344 487 pop,GOP 0.34291581108829566\n",
      "5724 0.7320216453220175 460 pop,GOP 0.3521739130434783\n",
      "5922 0.7685365775204881 407 pop,GOP 0.12285012285012285\n",
      "5930 0.7990434291994896 599 pop,GOP 0.20534223706176963\n",
      "6965 0.7702942644205278 870 pop,GOP 0.29195402298850576\n",
      "6977 0.7341962670994677 572 pop,GOP 0.3339160839160839\n",
      "6991 0.7963163132490593 1011 pop,GOP 0.3283877349159248\n",
      "6998 0.7511968282067665 658 pop,GOP 0.31155015197568386\n",
      "7002 0.7530428969521201 522 pop,GOP 0.24904214559386972\n",
      "7277 0.7906335864157763 705 pop,GOP 0.2652482269503546\n",
      "7662 0.7877106335444656 702 pop,GOP 0.14102564102564102\n",
      "7790 0.749137219541527 551 pop,GOP 0.3448275862068966\n",
      "8760 0.7609276763630126 221 pop,GOP 0.7013574660633484\n",
      "8761 0.715479861872609 190 pop,GOP 0.7157894736842105\n",
      "8763 0.6837485602165955 264 pop,GOP 0.7878787878787878\n",
      "8766 0.7279262148523326 308 pop,GOP 0.5746753246753247\n",
      "8775 0.7409079958314896 383 pop,GOP 0.6971279373368147\n",
      "8776 0.6877668937582269 622 pop,GOP 0.6639871382636656\n",
      "8779 0.7809961163207626 300 pop,GOP 0.6933333333333334\n",
      "8959 0.7975560650799327 162 pop,GOP 0.6851851851851852\n",
      "8960 0.7969497529451994 620 pop,GOP 0.7\n",
      "8961 0.7986077217335609 336 pop,GOP 0.7678571428571429\n",
      "8962 0.7929794252936739 250 pop,GOP 0.7\n",
      "9026 0.7951744486710951 173 pop,GOP 0.7225433526011561\n",
      "9028 0.7920685648004253 305 pop,GOP 0.7508196721311475\n",
      "9400 9.227882471823363e-18 81 pop,GOP 0.6296296296296297\n",
      "9944 0.747635147262188 749 pop,GOP 0.06542056074766354\n",
      "9947 0.7894441355541097 729 pop,GOP 0.05898491083676269\n",
      "9948 0.7577944230053142 436 pop,GOP 0.06651376146788991\n",
      "9949 0.7812621702914622 326 pop,GOP 0.10429447852760736\n",
      "9955 0.6772051023487804 1413 pop,GOP 0.6043878273177636\n",
      "9956 0.7334226632139759 914 pop,GOP 0.4485776805251641\n",
      "9958 0.6720961289266159 388 pop,GOP 0.5025773195876289\n",
      "9959 0.757384982667893 365 pop,GOP 0.4904109589041096\n",
      "9960 0.776172834982442 597 pop,GOP 0.4723618090452261\n",
      "9961 0.7848498089357092 353 pop,GOP 0.49291784702549574\n",
      "9962 0.6891419129706589 736 pop,GOP 0.561141304347826\n",
      "9963 0.7620046478583125 1183 pop,GOP 0.6170752324598479\n",
      "9964 0.7988591285866501 739 pop,GOP 0.5723951285520974\n",
      "9965 0.6979296670978298 486 pop,GOP 0.7283950617283951\n",
      "9967 0.6685201241035644 110 pop,GOP 0.08181818181818182\n",
      "9968 0.7892552395167989 650 pop,GOP 0.42615384615384616\n",
      "9969 0.6664150885181238 254 pop,GOP 0.12992125984251968\n",
      "9970 0.6720971761025477 282 pop,GOP 0.12056737588652482\n",
      "9971 0.7356207649129687 393 pop,GOP 0.21119592875318066\n",
      "9972 0.7340042140502157 163 pop,GOP 0.2822085889570552\n",
      "9973 0.6767594501714009 134 pop,GOP 0.20149253731343283\n",
      "9974 0.7429430898187154 264 pop,GOP 0.42803030303030304\n",
      "9975 0.6775872138126943 357 pop,GOP 0.22408963585434175\n",
      "9976 0.6796674796393362 465 pop,GOP 0.21935483870967742\n",
      "9977 0.6821683603133085 290 pop,GOP 0.20689655172413793\n",
      "9978 0.7922292219015122 768 pop,GOP 0.4440104166666667\n",
      "9981 0.7973067056272823 462 pop,GOP 0.37012987012987014\n",
      "9985 0.6937673574210433 246 pop,GOP 0.23170731707317074\n",
      "9986 0.7411170044098357 333 pop,GOP 0.3483483483483483\n",
      "9989 0.7867838507385128 481 pop,GOP 0.4594594594594595\n",
      "9990 0.7336012284461252 652 pop,GOP 0.36349693251533743\n",
      "9991 0.7495914406237589 446 pop,GOP 0.34080717488789236\n",
      "9992 0.7325461300033864 736 pop,GOP 0.46603260869565216\n",
      "9994 0.6672401401864556 405 pop,GOP 0.23950617283950618\n",
      "9995 0.7516069644676188 661 pop,GOP 0.4493192133131619\n",
      "9997 0.7900768168618383 425 pop,GOP 0.4376470588235294\n",
      "9999 0.6704210713444989 389 pop,GOP 0.23393316195372751\n",
      "10000 0.6889995228888267 748 pop,GOP 0.32486631016042783\n",
      "10001 0.6874301168900744 383 pop,GOP 0.34464751958224543\n",
      "10002 0.7057018310707579 674 pop,GOP 0.40801186943620177\n",
      "10003 0.7488108317619014 621 pop,GOP 0.3752012882447665\n",
      "10004 0.7276641453452306 671 pop,GOP 0.3621460506706408\n",
      "10009 0.693358499976681 441 pop,GOP 0.671201814058957\n",
      "10011 0.6884005202642299 463 pop,GOP 0.6652267818574514\n",
      "10012 0.7194406340395736 357 pop,GOP 0.6358543417366946\n",
      "10015 0.7614119774111787 409 pop,GOP 0.3105134474327628\n",
      "10016 0.7615415349466053 422 pop,GOP 0.3104265402843602\n",
      "10017 0.7751100453177435 532 pop,GOP 0.3383458646616541\n",
      "10018 0.7715266579814739 454 pop,GOP 0.29515418502202645\n",
      "10019 0.7773511472256412 275 pop,GOP 0.38545454545454544\n",
      "10020 0.7831680764776181 425 pop,GOP 0.4047058823529412\n",
      "10021 0.7805927347899063 261 pop,GOP 0.47509578544061304\n",
      "10022 0.7808371087249439 451 pop,GOP 0.3924611973392461\n",
      "10023 0.7826064010798244 503 pop,GOP 0.3817097415506958\n",
      "10024 0.7578779908089249 573 pop,GOP 0.43804537521815007\n",
      "10025 0.7824292541342497 524 pop,GOP 0.43702290076335876\n",
      "10026 0.7880251277345116 270 pop,GOP 0.4\n",
      "10027 0.7919593300389633 516 pop,GOP 0.40891472868217055\n",
      "10042 0.7455036784323356 653 pop,GOP 0.37978560490045943\n",
      "10043 0.7795885217835332 537 pop,GOP 0.3500931098696462\n",
      "10044 0.7533743157361944 388 pop,GOP 0.2809278350515464\n",
      "10045 0.7526707973904705 468 pop,GOP 0.32051282051282054\n",
      "10046 0.7584766194174012 403 pop,GOP 0.36228287841191065\n",
      "10047 0.7481343396707621 623 pop,GOP 0.34991974317817015\n",
      "10048 0.7497181219842627 526 pop,GOP 0.3593155893536122\n",
      "10049 0.794652841985466 799 pop,GOP 0.4655819774718398\n",
      "10053 0.743899387755177 673 pop,GOP 0.42050520059435365\n",
      "10054 0.7470030932586617 544 pop,GOP 0.37316176470588236\n",
      "10055 0.7632561803131408 664 pop,GOP 0.5512048192771084\n",
      "10056 0.7965288865747244 978 pop,GOP 0.4652351738241309\n",
      "10058 0.7925896766882613 591 pop,GOP 0.34686971235194586\n",
      "10062 0.7491993725480187 336 pop,GOP 0.2708333333333333\n",
      "10063 0.7244079931962807 656 pop,GOP 0.3429878048780488\n",
      "10064 0.7863261444860697 368 pop,GOP 0.4157608695652174\n",
      "10067 0.6797513292333625 209 pop,GOP 0.3253588516746411\n",
      "10068 0.7439689091610525 493 pop,GOP 0.3509127789046653\n",
      "10069 0.7456194353954511 850 pop,GOP 0.4294117647058823\n",
      "10070 0.6997772536274207 662 pop,GOP 0.41389728096676737\n",
      "10077 0.7461352868862611 578 pop,GOP 0.5224913494809689\n",
      "10080 0.730179414976101 611 pop,GOP 0.4844517184942717\n",
      "10081 0.7270590230893377 686 pop,GOP 0.6064139941690962\n",
      "10082 0.7764934149988187 602 pop,GOP 0.5813953488372093\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in IL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09802602313812253 = IL std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10312133011473218 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for IL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.7725\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by -0.00648 0.40693 HD avg vs true 0.41341\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.15673, should have been 0.16204\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for IL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 1.409\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for IL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 3.266 IL seats out of 17\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the IL map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  753676.4705882353\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 1.705 1.979\n",
      "fractional expected GOP seats =  5.717  out of  17 seats for IL\n",
      "simpler expected GOP seats =  5.7913  out of  17 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
